]> Algorithms for computing Maximally Redundant Trees for IP/LDP Fast-Reroute Ericsson
Konyves Kalman krt 11 Budapest Hungary 1097 Gabor.Sandor.Enyedi@ericsson.com
Ericsson
Konyves Kalman krt 11 Budapest Hungary 1097 Andras.Csaszar@ericsson.com
Juniper Networks
10 Technology Park Drive Westford MA 01886 USA akatlas@juniper.net
Juniper Networks
1194 N. Mathilda Ave. Sunnyvale CA 94089 USA cbowers@juniper.net
University of Arizona
1230 E Speedway Blvd. Tucson USA 85721 AZ abishek@ece.arizona.edu
Routing Routing Area Working Group A complete solution for IP and LDP Fast-Reroute using Maximally Redundant Trees is presented in [I-D.ietf-rtgwg-mrt-frr-architecture]. This document defines the associated MRT Lowpoint algorithm that is used in the default MRT profile to compute both the necessary Maximally Redundant Trees with their associated next-hops and the alternates to select for MRT-FRR.
MRT Fast-Reroute requires that packets can be forwarded not only on the shortest-path tree, but also on two Maximally Redundant Trees (MRTs), referred to as the MRT-Blue and the MRT-Red. A router which experiences a local failure must also have pre-determined which alternate to use. This document defines how to compute these three things for use in MRT-FRR and describes the algorithm design decisions and rationale. The algorithm is based on those presented in and expanded in . The MRT Lowpoint algorithm is required for implementation when the default MRT profile is implemented. Just as packets routed on a hop-by-hop basis require that each router compute a shortest-path tree which is consistent, it is necessary for each router to compute the MRT-Blue next-hops and MRT-Red next-hops in a consistent fashion. This document defines the MRT Lowpoint algorithm to be used as a standard in the default MRT profile for MRT-FRR. As now, a router's FIB will contain primary next-hops for the current shortest-path tree for forwarding traffic. In addition, a router's FIB will contain primary next-hops for the MRT-Blue for forwarding received traffic on the MRT-Blue and primary next-hops for the MRT-Red for forwarding received traffic on the MRT-Red. What alternate next-hops a point-of-local-repair (PLR) selects need not be consistent - but loops must be prevented. To reduce congestion, it is possible for multiple alternate next-hops to be selected; in the context of MRT alternates, each of those alternate next-hops would be equal-cost paths. This document defines an algorithm for selecting an appropriate MRT alternate for consideration. Other alternates, e.g. LFAs that are downstream paths, may be prefered when available and that policy-based alternate selection process is not captured in this document.
[D] | | | | ^ | | | | | V | | V [R] [F] [C] [R] [F] [C] [R] [F] [C] | | | ^ ^ | | | | | | | V | [A]---[B]---| [A]-->[B] [A]---[B]<--| (a) (b) (c) a 2-connected graph MRT-Blue towards R MRT-Red towards R ]]>
Algorithms for computing MRTs can handle arbitrary network topologies where the whole network graph is not 2-connected, as in , as well as the easier case where the network graph is 2-connected (). Each MRT is a spanning tree. The pair of MRTs provide two paths from every node X to the root of the MRTs. Those paths share the minimum number of nodes and the minimum number of links. Each such shared node is a cut-vertex. Any shared links are cut-links.
[D]---| |---[J] | ^ | | | | | ^ V | | | V V V | [R] [F] [C]<--[G] | [R] [F] [C]<--[G] | ^ ^ ^ | ^ | | | | | | V | V | | [A]-->[B]---| |---[H] [A]<--[B]<--| [H] (b) MRT-Blue towards R (c) MRT-Red towards R ]]>
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in
A graph that reflects the network topology where all links connect exactly two nodes and broadcast links have been transformed into a pseudo-node representation (e.g. in OSPF, viewing a Network LSA as representing a pseudo-noe). A pair of trees where the path from any node X to the root R on the first tree is node-disjoint with the path from the same node X to the root along the second tree. These can be computed in 2-connected graphs. A pair of trees where the path from any node X to the root R along the first tree and the path from the same node X to the root along the second tree share the minimum number of nodes and the minimum number of links. Each such shared node is a cut-vertex. Any shared links are cut-links. Any RT is an MRT but many MRTs are not RTs. From the computing router, the set of routers that support a particular MRT profile and are connected. MRT-Red is used to describe one of the two MRTs; it is used to describe the associated forwarding topology and MT-ID. Specifically, MRT-Red is the decreasing MRT where links in the GADAG are taken in the direction from a higher topologically ordered node to a lower one. MRT-Blue is used to describe one of the two MRTs; it is used to describe the associated forwarding topology and MT-ID. Specifically, MRT-Blue is the increasing MRT where links in the GADAG are taken in the direction from a lower topologically ordered node to a higher one. A vertex whose removal partitions the network. A link whose removal partitions the network. A cut-link by definition must be connected between two cut-vertices. If there are multiple parallel links, then they are referred to as cut-links in this document if removing the set of parallel links would partition the network. A graph that has no cut-vertices. This is a graph that requires two nodes to be removed before the network is partitioned. A tree containing links that connects all nodes in the network graph. In the context of a spanning tree computed via a depth-first search, a back-edge is a link that connects a descendant of a node x with an ancestor of x. A maximal set of nodes that are 2-connected. In a network graph with at least one cut-vertex, there will be multiple 2-connected clusters. Either a 2-connected cluster, a cut-link, or an isolated vertex. Directed Acyclic Graph - a digraph containing no directed cycle. Almost Directed Acyclic Graph - a digraph that can be transformed into a DAG with removing a single node (the root node). A subset of an ADAG that doesn't yet contain all the nodes in the block. A partial ADAG is created during the MRT algorithm and then expanded until all nodes in the block are included and it is an ADAG. Generalized ADAG - a digraph, which has only ADAGs as all of its blocks. The root of such a block is the node closest to the global root (e.g. with uniform link costs). Depth-First Search A node n is a DFS ancestor of x if n is on the DFS-tree path from the DFS root to x. A node n is a DFS descendant of x if x is on the DFS-tree path from the DFS root to n. A path along not-yet-included-in-the-GADAG nodes that starts at a node that is already-included-in-the-GADAG and that ends at a node that is already-included-in-the-GADAG. The starting and ending nodes may be the same node if it is a cut-vertex. Indicates the relationship between X and Y in a partial order, such as found in a GADAG. X >> Y means that X is higher in the partial order than Y. Y << X means that Y is lower in the partial order than X. Indicates the relationship between X and Y in the total order, such as found via a topological sort. X > Y means that X is higher in the total order than Y. Y < X means that Y is lower in the total order than X. A node added to the network graph to represent a multi-homed prefix or routers outside the local MRT-fast-reroute-supporting island of routers. The key property of proxy-nodes is that traffic cannot transit them. In the GADAG, each link is marked as OUTGOING, INCOMING or both. Until the directionality of the link is determined, the link is marked as UNDIRECTED to indicate that its direction hasn't been determined. A link marked as OUTGOING has direction in the GADAG from the interface's router to the remote end. A link marked as INCOMING has direction in the GADAG from the remote end to the interface's router.
There are five key concepts that are critical for understanding the MRT Lowpoint algorithm and other algorithms for computing MRTs. The first is the idea of partially ordering the nodes in a network graph with regard to each other and to the GADAG root. The second is the idea of finding an ear of nodes and adding them in the correct direction. The third is the idea of a Low-Point value and how it can be used to identify cut-vertices and to find a second path towards the root. The fourth is the idea that a non-2-connected graph is made up of blocks, where a block is a 2-connected cluster, a cut-link or an isolated node. The fifth is the idea of a local-root for each node; this is used to compute ADAGs in each block.
Given any two nodes X and Y in a graph, a particular total order means that either X < Y or X > Y in that total order. An example would be a graph where the nodes are ranked based upon their unique IP loopback addresses. In a partial order, there may be some nodes for which it can't be determined whether X << Y or X >> Y. A partial order can be captured in a directed graph, as shown in . In a graphical representation, a link directed from X to Y indicates that X is a neighbor of Y in the network graph and X << Y.
[C]--->[D] C and F | ^ C and G | | C and H V | [F]--->[G]--->[H] ]]>
To compute MRTs, the root of the MRTs is at both the very bottom and the very top of the partial ordering. This means that from any node X, one can pick nodes higher in the order until the root is reached. Similarly, from any node X, one can pick nodes lower in the order until the root is reached. For instance, in , from G the higher nodes picked can be traced by following the directed links and are H, D, E and R. Similarly, from G the lower nodes picked can be traced by reversing the directed links and are F, B, A, and R. A graph that represents this modified partial order is no longer a DAG; it is termed an Almost DAG (ADAG) because if the links directed to the root were removed, it would be a DAG.
[C]--->[D] Unspecified Relationships: | ^ C and F | | C and G V | C and H [F]--->[G]--->[H] ]]>
Most importantly, if a node Y >> X, then Y can only appear on the increasing path from X to the root and never on the decreasing path. Similarly, if a node Z << X, then Z can only appear on the decreasing path from X to the root and never on the inceasing path. When following the increasing paths, it is possible to pick multiple higher nodes and still have the certainty that those paths will be disjoint from the decreasing paths. E.g. in the previous example node B has multiple possibilities to forward packets along an increasing path: it can either forward packets to C or F.
For simplicity, the basic idea of creating a GADAG by adding ears is described assuming that the network graph is a single 2-connected cluster so that an ADAG is sufficient. Generalizing to multiple blocks is done by considering the block-roots instead of the GADAG root - and the actual algorithm is given in . In order to understand the basic idea of finding an ADAG, first suppose that we have already a partial ADAG, which doesn't contain all the nodes in the block yet, and we want to extend it to cover all the nodes. Suppose that we find a path from a node X to Y such that X and Y are already contained by our partial ADAG, but all the remaining nodes along the path are not added to the ADAG yet. We refer to such a path as an ear. Recall that our ADAG is closely related to a partial order. More precisely, if we remove root R, the remaining DAG describes a partial order of the nodes. If we suppose that neither X nor Y is the root, we may be able to compare them. If one of them is definitely lesser with respect to our partial order (say X<<Y), we can add the new path to the ADAG in a direction from X to Y. As an example consider .
B---| A-->B---| (a) (b) (c) (a) A 2-connected graph (b) Partial ADAG (C is not included) (c) Resulting ADAG after adding path (or ear) B-C-D ]]>
In this partial ADAG, node C is not yet included. However, we can find path B-C-D, where both endpoints are contained by this partial ADAG (we say those nodes are "ready" in the following text), and the remaining node (node C) is not contained yet. If we remove R, the remaining DAG defines a partial order, and with respect to this partial order we can say that B<<D, so we can add the path to the ADAG in the direction from B to D (arcs B->C and C->D are added). If B >> D, we would add the same path in reverse direction. If in the partial order where an ear's two ends are X and Y, X << Y, then there must already be a directed path from X to Y in the ADAG. The ear must be added in a direction such that it doesn't create a cycle; therefore the ear must go from X to Y. In the case, when X and Y are not ordered with each other, we can select either direction for the ear. We have no restriction since neither of the directions can result in a cycle. In the corner case when one of the endpoints of an ear, say X, is the root (recall that the two endpoints must be different), we could use both directions again for the ear because the root can be considered both as smaller and as greater than Y. However, we strictly pick that direction in which the root is lower than Y. The logic for this decision is explained in A partial ADAG is started by finding a cycle from the root R back to itself. This can be done by selecting a non-ready neighbor N of R and then finding a path from N to R that doesn't use any links between R and N. The direction of the cycle can be assigned either way since it is starting the ordering. Once a partial ADAG is already present, it will always have a node that is not the root R in it. As a brief proof that a partial ADAG can always have ears added to it: just select a non-ready neighbor N of a ready node Q, such that Q is not the root R, find a path from N to the root R in the graph with Q removed. This path is an ear where the first node of the ear is Q, the next is N, then the path until the first ready node the path reached (that ready node is the other endpoint of the path). Since the graph is 2-connected, there must be a path from N to R without Q. It is always possible to select a non-ready neighbor N of a ready node Q so that Q is not the root R. Because the network is 2-connected, N must be connected to two different nodes and only one can be R. Because the initial cycle has already been added to the ADAG, there are ready nodes that are not R. Since the graph is 2-connected, while there are non-ready nodes, there must be a non-ready neighbor N of a ready node that is not R.
Algorithm merely requires that a cycle or ear be selected without specifying how. Regardless of the way of selecting the path, we will get an ADAG. The method used for finding and selecting the ears is important; shorter ears result in shorter paths along the MRTs. The MRT Lowpoint algorithm's method using Low-Point Inheritance is defined in . Other methods are described in the Appendices ( and ). As an example, consider again. First, we select the shortest cycle containing R, which can be R-A-B-F-D-E (uniform link costs were assumed), so we get to the situation depicted in (b). Finally, we find a node next to a ready node; that must be node C and assume we reached it from ready node B. We search a path from C to R without B in the original graph. The first ready node along this is node D, so the open ear is B-C-D. Since B<<D, we add arc B->C and C->D to the ADAG. Since all the nodes are ready, we stop at this point.
A basic way of computing a spanning tree on a network graph is to run a depth-first-search, such as given in . This tree has the important property that if there is a link (x, n), then either n is a DFS ancestor of x or n is a DFS descendant of x. In other words, either n is on the path from the root to x or x is on the path from the root to n.
global_variable: dfs_number DFS_Visit(node x, node parent) D(x) = dfs_number dfs_number += 1 x.dfs_parent = parent for each link (x, w) if D(w) is not set DFS_Visit(w, x) Run_DFS(node gadag_root) dfs_number = 0 DFS_Visit(gadag_root, NONE)
Given a node x, one can compute the minimal DFS number of the neighbours of x, i.e. min( D(w) if (x,w) is a link). This gives the earliest attachment point neighbouring x. What is interesting, though, is what is the earliest attachment point from x and x's descendants. This is what is determined by computing the Low-Point value. In order to compute the low point value, the network is traversed using DFS and the vertices are numbered based on the DFS walk. Let this number be represented as DFS(x). All the edges that lead to already visited nodes during DFS walk are back-edges. The back-edges are important because they give information about reachability of a node via another path. The low point number is calculated by finding: (DFS(x), Lowest DFS(n, x->n is a back-edge), Lowest Low(n, x->n is tree edge in DFS walk) ). A detailed algorithm for computing the low-point value is given in . illustrates how the lowpoint algorithm applies to a example graph.
From the low-point value and lowpoint parent, there are three very useful things which motivate our computation. First, if there is a child c of x such that L(c) >= D(x), then there are no paths in the network graph that go from c or its descendants to an ancestor of x - and therefore x is a cut-vertex. In , this can be seen by looking at the DFS children of C. C has two children - D and F and L(F) = 3 = D(C) so it is clear that C is a cut-vertex and F is in a block where C is the block's root. L(D) = 0 < 3 = D(C) so D has a path to the ancestors of C; in this case, D can go via E to reach R. Comparing the low-point values of all a node's DFS-children with the node's DFS-value is very useful because it allows identification of the cut-vertices and thus the blocks. Second, by repeatedly following the path given by lowpoint_parent, there is a path from x back to an ancestor of x that does not use the link [x, x.dfs_parent] in either direction. The full path need not be taken, but this gives a way of finding an initial cycle and then ears. Third, as seen in , even if L(x) < D(x), there may be a block that contains both the root and a DFS-child of a node while other DFS-children might be in different blocks. In this example, C's child D is in the same block as R while F is not. It is important to realize that the root of a block may also be the root of another block.
A key idea for an MRT algorithm is that any non-2-connected graph is made up by blocks (e.g. 2-connected clusters, cut-links, and/or isolated nodes). To compute GADAGs and thus MRTs, computation is done in each block to compute ADAGs or Redundant Trees and then those ADAGs or Redundant Trees are combined into a GADAG or MRT.
[B] [G]---| [L]-->[M] (b) MRT-Blue for destination R [E]---| [J]-------->[I] [P]-->[O] | | | V V V [R] [D]-->[C]<---[F] [H]<---[K] [N] ^ | ^ | ^ | | V | | | V [A]<-------[B] [G]<--| [L]<--[M] (c) MRT-Red for destionation R ]]>
Consider the example depicted in (a). In this figure, a special graph is presented, showing us all the ways 2-connected clusters can be connected. It has four blocks: block 1 contains R, A, B, C, D, E, block 2 contains C, F, G, H, I, J, block 3 contains K, L, M, N, O, P, and block 4 is a cut-link containing H and K. As can be observed, the first two blocks have one common node (node C) and blocks 2 and 3 do not have any common node, but they are connected through a cut-link that is block 4. No two blocks can have more than one common node, since two blocks with at least two common nodes would qualify as a single 2-connected cluster. Moreover, observe that if we want to get from one block to another, we must use a cut-vertex (the cut-vertices in this graph are C, H, K), regardless of the path selected, so we can say that all the paths from block 3 along the MRTs rooted at R will cross K first. This observation means that if we want to find a pair of MRTs rooted at R, then we need to build up a pair of RTs in block 3 with K as a root. Similarly, we need to find another pair of RTs in block 2 with C as a root, and finally, we need the last pair of RTs in block 1 with R as a root. When all the trees are selected, we can simply combine them; when a block is a cut-link (as in block 4), that cut-link is added in the same direction to both of the trees. The resulting trees are depicted in (b) and (c). Similarly, to create a GADAG it is sufficient to compute ADAGs in each block and connect them. It is necessary, therefore, to identify the cut-vertices, the blocks and identify the appropriate local-root to use for each block.
Each node in a network graph has a local-root, which is the cut-vertex (or root) in the same block that is closest to the root. The local-root is used to determine whether two nodes share a common block.
There are two different ways of computing the local-root for each node. The stand-alone method is given in and better illustrates the concept; it is used by the MRT algorithms given in the Appendices and . The MRT Lowpoint algorithm computes the local-root for a block as part of computing the GADAG using lowpoint inheritance; the essence of this computation is given in . Both methods for computing the local-root produce the same results.
Once the local-roots are known, two nodes X and Y are in a common block if and only if one of the following three conditions apply. Y's local-root is X's local-root : They are in the same block and neither is the cut-vertex closest to the root. Y's local-root is X: X is the cut-vertex closest to the root for Y's block Y is X's local-root: Y is the cut-vertex closest to the root for X's block Once we have computed the local-root for each node in the network graph, we can assign for each node, a block id that represents the block in which the node is present. This computation is shown in .
This algorithm computes one GADAG that is then used by a router to determine its MRT-Blue and MRT-Red next-hops to all destinations. Finally, based upon that information, alternates are selected for each next-hop to each destination. The different parts of this algorithm are described below. These work on a network graph after its interfaces have been ordered as per . Compute the local MRT Island for the particular MRT Profile. [See .] Select the root to use for the GADAG. [See .] Initialize all interfaces to UNDIRECTED. [See .] Compute the DFS value,e.g. D(x), and lowpoint value, L(x). [See .] Construct the GADAG. [See ] Assign directions to all interfaces that are still UNDIRECTED. [See .] From the computing router x, compute the next-hops for the MRT-Blue and MRT-Red. [See .] Identify alternates for each next-hop to each destination by determining which one of the blue MRT and the red MRT the computing router x should select. [See .]
To ensure consistency in computation, all routers MUST order interfaces identically down to the set of links with the same metric to the same neighboring node. This is necessary for the DFS in Lowpoint_Visit in , where the selection order of the interfaces to explore results in different trees. Consistent interface ordering is also necessary for computing the GADAG, where the selection order of the interfaces to use to form ears can result in different GADAGs. It is also necessary for the topological sort described in , where different topological sort orderings can result in undirected links being added to the GADAG in different directions. The required ordering between two interfaces from the same router x is given in .
In , if two interfaces on a router connect to the same remote router with the same metric, the Interface_Compare function returns A_EQUAL_TO_B. This is because the order in which those interfaces are initially explored does not affect the final GADAG produced by the algorithm described here. While only one of the links will be added to the GADAG in the initial traversal, the other parallel links will be added to the GADAG with the same direction assigned during the procedure for assigning direction to UNDIRECTED links described in . An implementation is free to apply some additional criteria to break ties in interface ordering in this situation, but that criteria is not specified here since it will not affect the final GADAG produced by the algorithm. The Interface_Compare function in relies on the interface.metric and the interface.neighbor.mrt_node_id values to order interfaces. The exact source of these values for different IGPs (or flooding protocol in the case of ISIS-PCR ) and applications is specified in . The metric and mrt_node_id values for OSPFv2, OSPFv3, and IS-IS provided here is normative. The metric and mrt_node_id values for ISIS-PCR should be considered informational.
The metrics are unsigned integers and MUST be compared as unsigned integers. The results of mrt_node_id comparisons MUST be the same as would be obtained by converting the mrt_node_ids to unsigned integers using network byte order and performing the comparison as unsigned integers. Also note that these values are only specified in the case of point-to-point links. Therefore, in the case of IS-IS for IP/LDP FRR, the pseudonode number (the 7th octet) will always be zero. In the case of IS-IS for IP/LDP FRR, this specification allows for the use of Multi-Topology routing. requires that information related to the standard/default topology (MT-ID = 0) be carried in the Extended IS Reachability TLV #22, while it requires that the Multi-Topology IS Neighbor TLV #222 only be used to carry topology information related to non-default topologies (with non-zero MT-IDs). enforces this by requiring an implementation to ignore TLV#222 with MT-ID = 0. The current document also requires that TLV#222 with MT-ID = 0 MUST be ignored.
The local MRT Island for a particular MRT profile can be determined by starting from the computing router in the network graph and doing a breadth-first-search (BFS). The BFS explores only links that are in the same area/level, are not IGP-excluded, and are not MRT-ineligible. The BFS explores only nodes that are are not IGP-excluded, and that support the particular MRT profile. See section 7 of for more precise definitions of these criteria.
In Section 8.3 of , the GADAG Root Selection Policy is described for the MRT default profile. In and , a mechanism is given for routers to advertise the GADAG Root Selection Priority and consistently select a GADAG Root inside the local MRT Island. The MRT Lowpoint algorithm simply requires that all routers in the MRT Island MUST select the same GADAG Root; the mechanism can vary based upon the MRT profile description. Before beginning computation, the network graph is reduced to contain only the set of routers that support the specific MRT profile whose MRTs are being computed. Analysis has shown that the centrality of a router can have a significant impact on the lengths of the alternate paths computed. Therefore, it is RECOMMENDED that off-line analysis that considers the centrality of a router be used to help determine how good a choice a particular router is for the role of GADAG root.
Before running the algorithm, there is the standard type of initialization to be done, such as clearing any computed DFS-values, lowpoint-values, DFS-parents, lowpoint-parents, any MRT-computed next-hops, and flags associated with algorithm. It is assumed that a regular SPF computation has been run so that the primary next-hops from the computing router to each destination are known. This is required for determining alternates at the last step. Initially, all interfaces MUST be initialized to UNDIRECTED. Whether they are OUTGOING, INCOMING or both is determined when the GADAG is constructed and augmented. It is possible that some links and nodes will be marked as unusable using standard IGP mechanisms (see section 7 of ). Due to FRR manageability considerations , it may also be desirable to administratively configure some interfaces as ineligible to carry MRT FRR traffic. This constraint MUST be consistently flooded via the IGP by the owner of the interface, so that links are clearly known to be MRT-ineligible and not explored or used in the MRT algorithm. In the algorithm description, it is assumed that such links and nodes will not be explored or used, and no more discussion is given of this restriction.
As discussed in , it is necessary to find ears from a node x that is already in the GADAG (known as IN_GADAG). Two different methods are used to find ears in the algorithm. The first is by going to a not IN_GADAG DFS-child and then following the chain of low-point parents until an IN_GADAG node is found. The second is by going to a not IN_GADAG neighbor and then following the chain of DFS parents until an IN_GADAG node is found. As an ear is found, the associated interfaces are marked based on the direction taken. The nodes in the ear are marked as IN_GADAG. In the algorithm, first the ears via DFS-children are found and then the ears via DFS-neighbors are found. By adding both types of ears when an IN_GADAG node is processed, all ears that connect to that node are found. The order in which the IN_GADAG nodes is processed is, of course, key to the algorithm. The order is a stack of ears so the most recent ear is found at the top of the stack. Of course, the stack stores nodes and not ears, so an ordered list of nodes, from the first node in the ear to the last node in the ear, is created as the ear is explored and then that list is pushed onto the stack. Each ear represents a partial order (see ) and processing the nodes in order along each ear ensures that all ears connecting to a node are found before a node higher in the partial order has its ears explored. This means that the direction of the links in the ear is always from the node x being processed towards the other end of the ear. Additionally, by using a stack of ears, this means that any unprocessed nodes in previous ears can only be ordered higher than nodes in the ears below it on the stack. In this algorithm that depends upon Low-Point inheritance, it is necessary that every node have a low-point parent that is not itself. If a node is a cut-vertex, that may not yet be the case. Therefore, any nodes without a low-point parent will have their low-point parent set to their DFS parent and their low-point value set to the DFS-value of their parent. This assignment also properly allows an ear between two cut-vertices. Finally, the algorithm simultaneously computes each node's local-root, as described in . This is further elaborated as follows. The local-root can be inherited from the node at the end of the ear unless the end of the ear is x itself, in which case the local-root for all the nodes in the ear would be x. This is because whenever the first cycle is found in a block, or an ear involving a bridge is computed, the cut-vertex closest to the root would be x itself. In all other scenarios, the properties of lowpoint/dfs parents ensure that the end of the ear will be in the same block, and thus inheriting its local-root would be the correct local-root for all newly added nodes. The pseudo-code for the GADAG algorithm (assuming that the adjustment of lowpoint for cut-vertices has been made) is shown in .
The GADAG, regardless of the algorithm used to construct it, at this point could be used to find MRTs, but the topology does not include all links in the network graph. That has two impacts. First, there might be shorter paths that respect the GADAG partial ordering and so the alternate paths would not be as short as possible. Second, there may be additional paths between a router x and the root that are not included in the GADAG. Including those provides potentially more bandwidth to traffic flowing on the alternates and may reduce congestion compared to just using the GADAG as currently constructed. The goal is thus to assign direction to every remaining link marked as UNDIRECTED to improve the paths and number of paths found when the MRTs are computed. To do this, we need to establish a total order that respects the partial order described by the GADAG. This can be done using Kahn's topological sort which essentially assigns a number to a node x only after all nodes before it (e.g. with a link incoming to x) have had their numbers assigned. The only issue with the topological sort is that it works on DAGs and not ADAGs or GADAGs. To convert a GADAG to a DAG, it is necessary to remove all links that point to a root of block from within that block. That provides the necessary conversion to a DAG and then a topological sort can be done. When adding undirected links to the GADAG, links connecting the block root to other nodes in that block need special handling because the topological order will not always give the right answer for those links. There are three cases to consider. If the undirected link in question has another parallel link between the same two nodes that is already directed, then the direction of the undirected link can be inherited from the previously directed link. In the case of parallel cut links, we set all of the parallel links to both INCOMING and OUTGOING. Otherwise, the undirected link in question is set to OUTGOING from the block root node. A cut-link can then be identified by the fact that it will be directed both INCOMING and OUTGOING in the GADAG. The exact details of this whole process are captured in
Proxy-nodes do not need to be added to the network graph. They cannot be transited and do not affect the MRTs that are computed. The details of how the MRT-Blue and MRT-Red next-hops are computed for proxy-nodes and how the appropriate alternate next-hops are selected is given in .
As was discussed in , once a ADAG is found, it is straightforward to find the next-hops from any node X to the ADAG root. However, in this algorithm, we will reuse the common GADAG and find not only the one pair of MRTs rooted at the GADAG root with it, but find a pair rooted at each node. This is useful since it is significantly faster to compute. The method for computing differently rooted MRTs from the common GADAG is based on two ideas. First, if two nodes X and Y are ordered with respect to each other in the partial order, then an SPF along OUTGOING links (an increasing-SPF) and an SPF along INCOMING links (a decreasing-SPF) can be used to find the increasing and decreasing paths. Second, if two nodes X and Y aren't ordered with respect to each other in the partial order, then intermediary nodes can be used to create the paths by increasing/decreasing to the intermediary and then decreasing/increasing to reach Y. As usual, the two basic ideas will be discussed assuming the network is two-connected. The generalization to multiple blocks is discussed in . The full algorithm is given in .
To find two node-disjoint paths from the computing router X to any node Y, depends upon whether Y >> X or Y << X. As shown in , if Y >> X, then there is an increasing path that goes from X to Y without crossing R; this contains nodes in the interval [X,Y]. There is also a decreasing path that decreases towards R and then decreases from R to Y; this contains nodes in the interval [X,R-small] or [R-great,Y]. The two paths cannot have common nodes other than X and Y.
[R]--->(Cloud 1) MRT-Blue path: X->Cloud 2->Y MRT-Red path: X->Cloud 1->R->Cloud 3->Y ]]>
Similar logic applies if Y << X, as shown in . In this case, the increasing path from X increases to R and then increases from R to Y to use nodes in the intervals [X,R-great] and [R-small, Y]. The decreasing path from X reaches Y without crossing R and uses nodes in the interval [Y,X].
[R]--->(Cloud 1) MRT-Blue path: X->Cloud 3->R->Cloud 1->Y MRT-Red path: X->Cloud 2->Y ]]>
When X and Y are not ordered, the first path should increase until we get to a node G, where G >> Y. At G, we need to decrease to Y. The other path should be just the opposite: we must decrease until we get to a node H, where H << Y, and then increase. Since R is smaller and greater than Y, such G and H must exist. It is also easy to see that these two paths must be node disjoint: the first path contains nodes in interval [X,G] and [Y,G], while the second path contains nodes in interval [H,X] and [H,Y]. This is illustrated in . It is necessary to decrease and then increase for the MRT-Blue and increase and then decrease for the MRT-Red; if one simply increased for one and decreased for the other, then both paths would go through the root R.
(Cloud 4)--->[R]--->(Cloud 1)--->[H] ^ | | | | | (Cloud 3)<---[X]<---(Cloud 2)<-----------| MRT-Blue path: decrease to H and increase to Y X->Cloud 2->H->Cloud 5->Y MRT-Red path: increase to G and decrease to Y X->Cloud 3->G->Cloud 6->Y ]]>
This gives disjoint paths as long as G and H are not the same node. Since G >> Y and H << Y, if G and H could be the same node, that would have to be the root R. This is not possible because there is only one incoming interface to the root R which is created when the initial cycle is found. Recall from that whenever an ear was found to have an end that was the root R, the ear was directed from R so that the associated interface on R is outgoing and not incoming. Therefore, there must be exactly one node M which is the largest one before R, so the MRT-Red path will never reach R; it will turn at M and decrease to Y.
The basic ideas for computing RT next-hops in a 2-connected graph were given in and . Given these two ideas, how can we find the trees? If some node X only wants to find the next-hops (which is usually the case for IP networks), it is enough to find which nodes are greater and less than X, and which are not ordered; this can be done by running an increasing-SPF and a decreasing-SPF rooted at X and not exploring any links from the ADAG root. In principle, an traversal method other than SPF could be used to traverse the GADAG in the process of determining blue and red next-hops that result in maximally redundant trees. This will be the case as long as one traversal uses the links in the direction specified by the GADAG and the other traversal uses the links in the direction opposite of that specified by the GADAG. However, a different traversal algorithm will generally result in different blue and red next-hops. Therefore, the algorithm specified here requires the use of SPF to traverse the GADAG to generate MRT blue and red next-hops, as described below. An increasing-SPF rooted at X and not exploring links from the root will find the increasing next-hops to all Y >> X. Those increasing next-hops are X's next-hops on the MRT-Blue to reach Y. A decreasing-SPF rooted at X and not exploring links from the root will find the decreasing next-hops to all Z << X. Those decreasing next-hops are X's next-hops on the MRT-Red to reach Z. Since the root R is both greater than and less than X, after this increasing-SPF and decreasing-SPF, X's next-hops on the MRT-Blue and on the MRT-Red to reach R are known. For every node Y >> X, X's next-hops on the MRT-Red to reach Y are set to those on the MRT-Red to reach R. For every node Z << X, X's next-hops on the MRT-Blue to reach Z are set to those on the MRT-Blue to reach R. For those nodes which were not reached by either the increasing-SPF or the decreasing-SPF, we can determine the next-hops as well. The increasing MRT-Blue next-hop for a node which is not ordered with respect to X is the next-hop along the decreasing MRT-Red towards R, and the decreasing MRT-Red next-hop is the next-hop along the increasing MRT-Blue towards R. Naturally, since R is ordered with respect to all the nodes, there will always be an increasing and a decreasing path towards it. This algorithm does not provide the complete specific path taken but just the appropriate next-hops to use. The identities of G and H are not determined by the computing node X. The final case to considered is when the root R computes its own next-hops. Since the root R is << all other nodes, running an increasing-SPF rooted at R will reach all other nodes; the MRT-Blue next-hops are those found with this increasing-SPF. Similarly, since the root R is >> all other nodes, running a decreasing-SPF rooted at R will reach all other nodes; the MRT-Red next-hops are those found with this decreasing-SPF.
B---| (a) (b) A 2-connected graph A spanning ADAG rooted at R ]]>
As an example consider the situation depicted in . Node C runs an increasing-SPF and a decreasing-SPF on the ADAG. The increasing-SPF reaches D, E and R and the decreasing-SPF reaches B, A and R. E>>C. So towards E the MRT-Blue next-hop is D, since E was reached on the increasing path through D. And the MRT-Red next-hop towards E is B, since R was reached on the decreasing path through B. Since E>>D, D will similarly compute its MRT-Blue next-hop to be E, ensuring that a packet on MRT-Blue will use path C-D-E. B, A and R will similarly compute the MRT-Red next-hops towards E (which is ordered less than B, A and R), ensuring that a packet on MRT-Red will use path C-B-A-R-E. C can determine the next-hops towards F as well. Since F is not ordered with respect to C, the MRT-Blue next-hop is the decreasing one towards R (which is B) and the MRT-Red next-hop is the increasing one towards R (which is D). Since F>>B, for its MRT-Blue next-hop towards F, B will use the real increasing next-hop towards F. So a packet forwarded to B on MRT-Blue will get to F on path C-B-F. Similarly, D will use the real decreasing next-hop towards F as its MRT-Red next-hop, a packet on MRT-Red will use path C-D-F.
If a graph isn't 2-connected, then the basic approach given in needs some extensions to determine the appropriate MRT next-hops to use for destinations outside the computing router X's blocks. In order to find a pair of maximally redundant trees in that graph we need to find a pair of RTs in each of the blocks (the root of these trees will be discussed later), and combine them. When computing the MRT next-hops from a router X, there are three basic differences: Only nodes in a common block with X should be explored in the increasing-SPF and decreasing-SPF. Instead of using the GADAG root, X's local-root should be used. This has the following implications: The links from X's local-root should not be explored. If a node is explored in the outgoing SPF so Y >> X, then X's MRT-Red next-hops to reach Y uses X's MRT-Red next-hops to reach X's local-root and if Z << X, then X's MRT-Blue next-hops to reach Z uses X's MRT-Blue next-hops to reach X's local-root. If a node W in a common block with X was not reached in the increasing-SPF or decreasing-SPF, then W is unordered with respect to X. X's MRT-Blue next-hops to W are X's decreasing (aka MRT-Red) next-hops to X's local-root. X's MRT-Red next-hops to W are X's increasing (aka MRT-Blue) next-hops to X's local-root. For nodes in different blocks, the next-hops must be inherited via the relevant cut-vertex. These are all captured in the detailed algorithm given in .
The complete algorithm to compute MRT Next-Hops for a particular router X is given in . In addition to computing the MRT-Blue next-hops and MRT-Red next-hops used by X to reach each node Y, the algorithm also stores an "order_proxy", which is the proper cut-vertex to reach Y if it is outside the block, and which is used later in deciding whether the MRT-Blue or the MRT-Red can provide an acceptable alternate for a particular primary next-hop.
At this point, a computing router S knows its MRT-Blue next-hops and MRT-Red next-hops for each destination in the MRT Island. The primary next-hops along the SPT are also known. It remains to determine for each primary next-hop to a destination D, which of the MRTs avoids the primary next-hop node F. This computation depends upon data set in Compute_MRT_NextHops such as each node y's y.blue_next_hops, y.red_next_hops, y.order_proxy, y.higher, y.lower and topo_orders. Recall that any router knows only which are the nodes greater and lesser than itself, but it cannot decide the relation between any two given nodes easily; that is why we need topological ordering. For each primary next-hop node F to each destination D, S can call Select_Alternates(S, D, F, primary_intf) to determine whether to use the MRT-Blue or MRT-Red next-hops as the alternate next-hop(s) for that primary next hop. The algorithm is given in and discussed afterwards.
D_topo_order) return USE_BLUE if (F.topo_order < D_topo_order) return USE_RED else if D_lower if F.HIGHER and F.LOWER return USE_RED if F.HIGHER return USE_RED if F.LOWER if F.topo_order > D_topo_order return USE_BLUE if F.topo_order < D_topo_order return USE_RED else //D is unordered wrt S if F.HIGHER and F.LOWER if primary_intf.OUTGOING and primary_intf.INCOMING // this case should not occur if primary_intf.OUTGOING return USE_BLUE if primary_intf.INCOMING return USE_RED if F.LOWER return USE_RED if F.HIGHER return USE_BLUE Select_Alternates(D, F, primary_intf) if (D is F) or (D.order_proxy is F) return PRIM_NH_IS_D_OR_OP_FOR_D D_lower = D.order_proxy.LOWER D_higher = D.order_proxy.HIGHER D_topo_order = D.order_proxy.topo_order return Select_Alternates_Internal(D, F, primary_intf, D_lower, D_higher, D_topo_order) ]]>
It is useful to first handle the case where where F is also D, or F is the order proxy for D. In this case, only link protection is possible. The MRT that doesn't use the failed primary next-hop is used. If both MRTs use the primary next-hop, then the primary next-hop must be a cut-link, so either MRT could be used but the set of MRT next-hops must be pruned to avoid the failed primary next-hop interface. To indicate this case, Select_Alternates returns PRIM_NH_IS_D_OR_OP_FOR_D. Explicit pseudocode to handle the three sub-cases above is not provided. The logic behind Select_Alternates_Internal is described in . As an example, consider the first case described in the table, where the D>>S and D<<S. If this is true, then either S or D must be the block root, R. If F>>S and F<<S, then S is the block root. So the blue path from S to D is the increasing path to D, and the red path S to D is the decreasing path to D. If the F.topo_order<D.topo_order, then either F is ordered higher than D or F is unordered with respect to D. Therefore, F is either on a decreasing path from S to D, or it is on neither an increasing nor a decreasing path from S to D. In either case, it is safe to take an increasing path from S to D to avoid F. We know that when S is R, the increasing path is the blue path, so it is safe to use the blue path to avoid F. If instead F.topo_order>D.topo_order, then either F is ordered lower than D, or F is unordered with respect to D. Therefore, F is either on an increasing path from S to D, or it is on neither an increasing nor a decreasing path from S to D. In either case, it is safe to take a decreasing path from S to D to avoid F. We know that when S is R, the decreasing path is the red path, so it is safe to use the red path to avoid F. If F>>S or F<<S (but not both), then D is the block root. We then know that the blue path from S to D is the increasing path to R, and the red path is the decreasing path to R. When F>>S, we deduce that F is on an increasing path from S to R. So in order to avoid F, we use a decreasing path from S to R, which is the red path. Instead, when F<<S, we deduce that F is on a decreasing path from S to R. So in order to avoid F, we use an increasing path from S to R, which is the blue path. All possible cases are systematically described in the same manner in the rest of the table.
>S | Blue path: | F>>S | additional | F on an | Use Red | | and | Increasing | only | criteria | increasing | to avoid | | D<>S | topo(F)>topo(D) | F on a | Use Blue | | S is | Blue path: | and | implies that | decreasing | to avoid | | R | Increasing | F<>D or F??D | path from | F | | | path to D. | | | S to D or | | | | Red path: | | | neither | | | | Decreasing | +-----------------+------------+------------+ | | path to D. | | topo(F)>S | Blue path: | F<>S | topo(F)>topo(D) | F on | Use Blue | | | Decreasing | only | implies that | decreasing | to avoid | | | shortest | | F>>D or F??D | path from | F | | | path from | | | R to D | | | | S to R, | | | or | | | | then | | | neither | | | | decreasing | +-----------------+------------+------------+ | | shortest | | topo(F)>S | additional | F on Red | Use Blue | | | | and | criteria | | to avoid | | | | F<>S | additional | F on | Use Red | | only | Increasing | only | criteria | increasing | to avoid | | | shortest | | not needed | path from | F | | | path from | | | S to R | | | | S to R, +------+-----------------+------------+------------+ | | then | F<topo(D) | F on | Use Blue | | | increasing | only | implies that | decreasing | to avoid | | | shortest | | F>>D or F??D | path from | F | | | path from | | | R to D | | | | R to D. | | | or | | | | Red path: | | | neither | | | | Decreasing | +-----------------+------------+------------+ | | shortest | | topo(F)>S | additional | F on Blue | Use Red | | | | and | criteria | | to avoid | | | | F<>D, | | | S to H. | | | | then incr. +------+-----------------+------------+------------+ | | to D. | F>>S | additional | F on an | Use Blue | | | Red path: | only | criteria | increasing | to avoid | | | Incr. from | | not needed | path from | F | | | S to first | | | S to G | | | | node G<>S | GADAG link | F on an | Use Blue | | | | and | direction | incr. path | to avoid | | | | F<F | from S | F | | | | F is +-----------------+------------+------------+ | | | R | GADAG link | F on a | Use Red | | | | | direction | decr. path | to avoid | | | | | S<-F | from S | F | | | | +-----------------+------------+------------+ | | | | GADAG link | Implies F is the order | | | | | direction | proxy for D, which has | | | | | S<-->F | already been handled. | +------+------------+------+-----------------+------------+------------+ ]]>
As an example, consider the ADAG depicted in and first suppose that G is the source, D is the destination and H is the failed next-hop. Since D>>G, we need to compare H.topo_order and D.topo_order. Since D.topo_order>H.topo_order, D must be not smaller than H, so we should select the decreasing path towards the root. If, however, the destination were instead J, we must find that H.topo_order>J.topo_order, so we must choose the increasing Blue next-hop to J, which is I. In the case, when instead the destination is C, we find that we need to first decrease to avoid using H, so the Blue, first decreasing then increasing, path is selected.
[I] | ^ ^ ^ V | | | [A]->[B]->[F]---| (a)ADAG rooted at R for a 2-connected graph ]]>
As discussed in Section 10.2 of , it is necessary to find MRT-Blue and MRT-Red next-hops and MRT-FRR alternates for a named proxy-nodes. An example case is for a router that is not part of that local MRT Island, when there is only partial MRT support in the domain. A first incorrect and naive approach to handling proxy-nodes, which cannot be transited, is to simply add these proxy-nodes to the graph of the network and connect it to the routers through which the new proxy-node can be reached. Unfortunately, this can introduce some new ordering between the border routers connected to the new node which could result in routing MRT paths through the proxy-node. Thus, this naive approach would need to recompute GADAGs and redo SPTs for each proxy-node. Instead of adding the proxy-node to the original network graph, each individual proxy-node can be individually added to the GADAG. The proxy-node is connected to at most two nodes in the GADAG. Section 10.2 of defines how the proxy-node attachments MUST be determined. The degenerate case where the proxy-node is attached to only one node in the GADAG is trivial as all needed information can be derived from that attachment node; if there are different interfaces, then some can be assigned to MRT-Red and others to MRT_Blue. Now, consider the proxy-node that is attached to exactly two nodes in the GADAG. Let the order_proxies of these nodes be A and B. Let the current node, where next-hop is just being calculated, be S. If one of these two nodes A and B is the local root of S, let A=S.local_root and the other one be B. Otherwise, let A.topo_order < B.topo_order. A valid GADAG was constructed. Instead doing an increasing-SPF and a decreasing-SPF to find ordering for the proxy-nodes, the following simple rules, providing the same result, can be used independently for each different proxy-node. For the following rules, let X=A.local_root, and if A is the local root, let that be strictly lower than any other node. Always take the first rule that matches.
>B Blue to R Red to B 4 A<
These rules are realized in the following pseudocode where P is the proxy-node, X and Y are the nodes that P is attached to, and S is the computing router:
After finding the the red and the blue next-hops, it is necessary to know which one of these to use in the case of failure. This can be done by Select_Alternates_Inner(). In order to use Select_Alternates_Internal(), we need to know if P is greater, less or unordered with S, and P.topo_order. P.lower = B.lower, P.higher = A.higher, and any value is OK for P.topo_order, as long as A.topo_order<=P.topo_order<=B.topo_order and P.topo_order is not equal to the topo_order of the failed node. So for simplicity let P.topo_order=A.topo_order when the next-hop is not A, and P.topo_order=B.topo_order otherwise. This gives the following pseudo-code:
This specification defines the MRT Lowpoint Algorithm, which include the construction of a common GADAG and the computation of MRT-Red and MRT-Blue next-hops to each node in the graph. An implementation MAY select any subset of next-hops for MRT-Red and MRT-Blue that respect the available nodes that are described in for each of the MRT-Red and MRT-Blue and the selected next-hops are further along in the interval of allowed nodes towards the destination. For example, the MRT-Blue next-hops used when the destination Y >> X, the computing router, MUST be one or more nodes, T, whose topo_order is in the interval [X.topo_order, Y.topo_order] and where Y >> T or Y is T. Similarly, the MRT-Red next-hops MUST be have a topo_order in the interval [R-small.topo_order, X.topo_order] or [Y.topo_order, R-big.topo_order]. Implementations SHOULD implement the Select_Alternates() function to pick an MRT-FRR alternate.
Below is Python code implementing the MRT Lowpoint algorithm specified in this document. In order to avoid the page breaks in the .txt version of the draft, one can cut and paste the Python code from the .xml version. The code is also posted on Github.
# This program has been tested to run on Python 2.6 and 2.7 # (specifically Python 2.6.6 and 2.7.8 were tested). # The program has known incompatibilities with Python 3.X. # When executed, this program will generate a text file describing # an example topology. It then reads that text file back in as input # to create the example topology, and runs the MRT algorithm.This # was done to simplify the inclusion of the program as a single text # file that can be extracted from the IETF draft. # The output of the program is four text files containing a description # of the GADAG, the blue and red MRTs for all destinations, and the # MRT alternates for all failures. import heapq # simple Class definitions allow structure-like dot notation for # variables and a convenient place to initialize those variables. class Topology: pass class Node: pass class Interface: pass class Bundle: pass class Alternate: def __init__(self): self.failed_intf = None self.nh_list = [] self.fec = 'NO_ALTERNATE' self.prot = 'NO_PROTECTION' self.info = 'NONE' def Interface_Compare(intf_a, intf_b): if intf_a.metric < intf_b.metric: return -1 if intf_b.metric < intf_a.metric: return 1 if intf_a.remote_node.node_id < intf_b.remote_node.node_id: return -1 if intf_b.remote_node.node_id < intf_a.remote_node.node_id: return 1 return 0 def Sort_Interfaces(topo): for node in topo.island_node_list: node.island_intf_list.sort(Interface_Compare) def Initialize_Node(node): node.intf_list = [] node.island_intf_list = [] node.profile_id_list = [0] node.GR_sel_priority = 128 node.IN_MRT_ISLAND = False node.IN_GADAG = False node.dfs_number = None node.dfs_parent = None node.dfs_parent_intf = None node.dfs_child_list = [] node.lowpoint_number = None node.lowpoint_parent = None node.lowpoint_parent_intf = None node.localroot = None node.block_id = None node.IS_CUT_VERTEX = False node.blue_next_hops_dict = {} node.red_next_hops_dict = {} node.pnh_dict = {} node.alt_dict = {} def Initialize_Intf(intf): intf.metric = None intf.area = None intf.MRT_INELIGIBLE = False intf.IGP_EXCLUDED = False intf.UNDIRECTED = True intf.INCOMING = False intf.OUTGOING = False intf.INCOMING_STORED = False intf.OUTGOING_STORED = False intf.PROCESSED = False intf.IN_MRT_ISLAND = False def Reset_Computed_Node_and_Intf_Values(topo): for node in topo.node_list: node.IN_MRT_ISLAND = False node.IN_GADAG = False node.dfs_number = None node.dfs_parent = None node.dfs_parent_intf = None node.dfs_child_list = [] node.lowpoint_number = None node.lowpoint_parent = None node.lowpoint_parent_intf = None node.localroot = None node.block_id = None node.IS_CUT_VERTEX = False for intf in node.intf_list: intf.UNDIRECTED = True intf.INCOMING = False intf.OUTGOING = False intf.INCOMING_STORED = False intf.OUTGOING_STORED = False intf.IN_MRT_ISLAND = False # This function takes a file with links represented by 2-digit # numbers in the format: # 01,05,10 # 05,02,30 # 02,01,15 # which represents a triangle topology with nodes 01, 05, and 02 # and symmetric metrics of 10, 30, and 15. # Inclusion of a fourth column makes the metrics for the link # asymmetric. An entry of: # 02,07,10,15 # creates a link from node 02 to 07 with metrics 10 and 15. def Create_Topology_From_File(filename): topo = Topology() topo.gadag_root = None topo.node_list = [] topo.node_dict = {} topo.island_node_list = [] topo.prefix_list = [] # possibly no longer needed node_id_set= set() cols_list = [] # on first pass just create nodes with open(filename) as topo_file: for line in topo_file: line = line.rstrip('\r\n') cols=line.split(',') cols_list.append(cols) nodea_node_id = int(cols[0]) nodeb_node_id = int(cols[1]) if (nodea_node_id > 999 or nodeb_node_id > 999): print("node_id must be between 0 and 999.") print("exiting.") exit() node_id_set.add(nodea_node_id) node_id_set.add(nodeb_node_id) for node_id in node_id_set: node = Node() node.node_id = node_id Initialize_Node(node) topo.node_list.append(node) topo.node_dict[node_id] = node # on second pass create interfaces for cols in cols_list: nodea_node_id = int(cols[0]) nodeb_node_id = int(cols[1]) metric = int(cols[2]) reverse_metric = int(cols[2]) if len(cols) > 3: reverse_metric=int(cols[3]) nodea = topo.node_dict[nodea_node_id] nodeb = topo.node_dict[nodeb_node_id] nodea_intf = Interface() Initialize_Intf(nodea_intf) nodea_intf.metric = metric nodea_intf.area = 0 nodeb_intf = Interface() Initialize_Intf(nodeb_intf) nodeb_intf.metric = reverse_metric nodeb_intf.area = 0 nodea_intf.remote_intf = nodeb_intf nodeb_intf.remote_intf = nodea_intf nodea_intf.remote_node = nodeb nodeb_intf.remote_node = nodea nodea_intf.local_node = nodea nodeb_intf.local_node = nodeb nodea_intf.link_data = len(nodea.intf_list) nodeb_intf.link_data = len(nodeb.intf_list) nodea.intf_list.append(nodea_intf) nodeb.intf_list.append(nodeb_intf) return topo def MRT_Island_Identification(topo, computing_rtr, profile_id, area): if profile_id in computing_rtr.profile_id_list: computing_rtr.IN_MRT_ISLAND = True explore_list = [computing_rtr] else: return while explore_list != []: next_rtr = explore_list.pop() for intf in next_rtr.intf_list: if ( not intf.MRT_INELIGIBLE and not intf.IGP_EXCLUDED and intf.area == area ): if (profile_id in intf.remote_node.profile_id_list): intf.IN_MRT_ISLAND = True if (not intf.remote_node.IN_MRT_ISLAND): intf.remote_node.IN_MRT_ISLAND = True explore_list.append(intf.remote_node) def Set_Island_Intf_and_Node_Lists(topo): topo.island_node_list = [] for node in topo.node_list: node.island_intf_list = [] if node.IN_MRT_ISLAND: topo.island_node_list.append(node) for intf in node.intf_list: if intf.IN_MRT_ISLAND: node.island_intf_list.append(intf) global_dfs_number = None def Lowpoint_Visit(x, parent, intf_p_to_x): global global_dfs_number x.dfs_number = global_dfs_number x.lowpoint_number = x.dfs_number global_dfs_number += 1 x.dfs_parent = parent if intf_p_to_x == None: x.dfs_parent_intf = None else: x.dfs_parent_intf = intf_p_to_x.remote_intf x.lowpoint_parent = None if parent != None: parent.dfs_child_list.append(x) for intf in x.island_intf_list: if intf.remote_node.dfs_number == None: Lowpoint_Visit(intf.remote_node, x, intf) if intf.remote_node.lowpoint_number < x.lowpoint_number: x.lowpoint_number = intf.remote_node.lowpoint_number x.lowpoint_parent = intf.remote_node x.lowpoint_parent_intf = intf else: if intf.remote_node is not parent: if intf.remote_node.dfs_number < x.lowpoint_number: x.lowpoint_number = intf.remote_node.dfs_number x.lowpoint_parent = intf.remote_node x.lowpoint_parent_intf = intf def Run_Lowpoint(topo): global global_dfs_number global_dfs_number = 0 Lowpoint_Visit(topo.gadag_root, None, None) # addresses these cases. max_block_id = None def Assign_Block_ID(x, cur_block_id): global max_block_id x.block_id = cur_block_id for c in x.dfs_child_list: if (c.localroot is x): max_block_id += 1 Assign_Block_ID(c, max_block_id) else: Assign_Block_ID(c, cur_block_id) def Run_Assign_Block_ID(topo): global max_block_id max_block_id = 0 Assign_Block_ID(topo.gadag_root, max_block_id) def Construct_Ear(x, stack, intf, ear_type): ear_list = [] cur_intf = intf not_done = True while not_done: cur_intf.UNDIRECTED = False cur_intf.OUTGOING = True cur_intf.remote_intf.UNDIRECTED = False cur_intf.remote_intf.INCOMING = True if cur_intf.remote_node.IN_GADAG == False: cur_intf.remote_node.IN_GADAG = True ear_list.append(cur_intf.remote_node) if ear_type == 'CHILD': cur_intf = cur_intf.remote_node.lowpoint_parent_intf else: assert ear_type == 'NEIGHBOR' cur_intf = cur_intf.remote_node.dfs_parent_intf else: not_done = False if ear_type == 'CHILD' and cur_intf.remote_node is x: # x is a cut-vertex and the local root for the block # in which the ear is computed x.IS_CUT_VERTEX = True localroot = x else: # inherit local root from the end of the ear localroot = cur_intf.remote_node.localroot while ear_list != []: y = ear_list.pop() y.localroot = localroot stack.append(y) def Construct_GADAG_via_Lowpoint(topo): gadag_root = topo.gadag_root gadag_root.IN_GADAG = True gadag_root.localroot = None stack = [] stack.append(gadag_root) while stack != []: x = stack.pop() for intf in x.island_intf_list: if ( intf.remote_node.IN_GADAG == False and intf.remote_node.dfs_parent is x ): Construct_Ear(x, stack, intf, 'CHILD' ) for intf in x.island_intf_list: if (intf.remote_node.IN_GADAG == False and intf.remote_node.dfs_parent is not x): Construct_Ear(x, stack, intf, 'NEIGHBOR') def Assign_Remaining_Lowpoint_Parents(topo): for node in topo.island_node_list: if ( node is not topo.gadag_root and node.lowpoint_parent == None ): node.lowpoint_parent = node.dfs_parent node.lowpoint_parent_intf = node.dfs_parent_intf node.lowpoint_number = node.dfs_parent.dfs_number def Add_Undirected_Block_Root_Links(topo): for node in topo.island_node_list: if node.IS_CUT_VERTEX or node is topo.gadag_root: for intf in node.island_intf_list: if ( intf.remote_node.localroot is not node or intf.PROCESSED ): continue bundle_list = [] bundle = Bundle() bundle.UNDIRECTED = True bundle.OUTGOING = False bundle.INCOMING = False for intf2 in node.island_intf_list: if intf2.remote_node is intf.remote_node: bundle_list.append(intf2) if not intf2.UNDIRECTED: bundle.UNDIRECTED = False if intf2.INCOMING: bundle.INCOMING = True if intf2.OUTGOING: bundle.OUTGOING = True if bundle.UNDIRECTED: for intf3 in bundle_list: intf3.UNDIRECTED = False intf3.remote_intf.UNDIRECTED = False intf3.PROCESSED = True intf3.remote_intf.PROCESSED = True intf3.OUTGOING = True intf3.remote_intf.INCOMING = True else: if (bundle.OUTGOING and bundle.INCOMING): for intf3 in bundle_list: intf3.UNDIRECTED = False intf3.remote_intf.UNDIRECTED = False intf3.PROCESSED = True intf3.remote_intf.PROCESSED = True intf3.OUTGOING = True intf3.INCOMING = True intf3.remote_intf.INCOMING = True intf3.remote_intf.OUTGOING = True elif bundle.OUTGOING: for intf3 in bundle_list: intf3.UNDIRECTED = False intf3.remote_intf.UNDIRECTED = False intf3.PROCESSED = True intf3.remote_intf.PROCESSED = True intf3.OUTGOING = True intf3.remote_intf.INCOMING = True elif bundle.INCOMING: for intf3 in bundle_list: intf3.UNDIRECTED = False intf3.remote_intf.UNDIRECTED = False intf3.PROCESSED = True intf3.remote_intf.PROCESSED = True intf3.INCOMING = True intf3.remote_intf.OUTGOING = True def Modify_Block_Root_Incoming_Links(topo): for node in topo.island_node_list: if ( node.IS_CUT_VERTEX == True or node is topo.gadag_root ): for intf in node.island_intf_list: if intf.remote_node.localroot is node: if intf.INCOMING: intf.INCOMING = False intf.INCOMING_STORED = True intf.remote_intf.OUTGOING = False intf.remote_intf.OUTGOING_STORED = True def Revert_Block_Root_Incoming_Links(topo): for node in topo.island_node_list: if ( node.IS_CUT_VERTEX == True or node is topo.gadag_root ): for intf in node.island_intf_list: if intf.remote_node.localroot is node: if intf.INCOMING_STORED: intf.INCOMING = True intf.remote_intf.OUTGOING = True intf.INCOMING_STORED = False intf.remote_intf.OUTGOING_STORED = False def Run_Topological_Sort_GADAG(topo): Modify_Block_Root_Incoming_Links(topo) for node in topo.island_node_list: node.unvisited = 0 for intf in node.island_intf_list: if (intf.INCOMING == True): node.unvisited += 1 working_list = [] topo_order_list = [] working_list.append(topo.gadag_root) while working_list != []: y = working_list.pop(0) topo_order_list.append(y) for intf in y.island_intf_list: if ( intf.OUTGOING == True): intf.remote_node.unvisited -= 1 if intf.remote_node.unvisited == 0: working_list.append(intf.remote_node) next_topo_order = 1 while topo_order_list != []: y = topo_order_list.pop(0) y.topo_order = next_topo_order next_topo_order += 1 Revert_Block_Root_Incoming_Links(topo) def Set_Other_Undirected_Links_Based_On_Topo_Order(topo): for node in topo.island_node_list: for intf in node.island_intf_list: if intf.UNDIRECTED: if node.topo_order < intf.remote_node.topo_order: intf.OUTGOING = True intf.UNDIRECTED = False intf.remote_intf.INCOMING = True intf.remote_intf.UNDIRECTED = False else: intf.INCOMING = True intf.UNDIRECTED = False intf.remote_intf.OUTGOING = True intf.remote_intf.UNDIRECTED = False def Initialize_Temporary_Interface_Flags(topo): for node in topo.island_node_list: for intf in node.island_intf_list: intf.PROCESSED = False intf.INCOMING_STORED = False intf.OUTGOING_STORED = False def Add_Undirected_Links(topo): Initialize_Temporary_Interface_Flags(topo) Add_Undirected_Block_Root_Links(topo) Run_Topological_Sort_GADAG(topo) Set_Other_Undirected_Links_Based_On_Topo_Order(topo) def In_Common_Block(x,y): if ( (x.block_id == y.block_id) or ( x is y.localroot) or (y is x.localroot) ): return True return False def Copy_List_Items(target_list, source_list): del target_list[:] # Python idiom to remove all elements of a list for element in source_list: target_list.append(element) def Add_Item_To_List_If_New(target_list, item): if item not in target_list: target_list.append(item) def Store_Results(y, direction): if direction == 'INCREASING': y.HIGHER = True Copy_List_Items(y.blue_next_hops, y.next_hops) if direction == 'DECREASING': y.LOWER = True Copy_List_Items(y.red_next_hops, y.next_hops) if direction == 'NORMAL_SPF': y.primary_spf_metric = y.spf_metric Copy_List_Items(y.primary_next_hops, y.next_hops) if direction == 'MRT_ISLAND_SPF': Copy_List_Items(y.mrt_island_next_hops, y.next_hops) if direction == 'COLLAPSED_SPF': y.collapsed_metric = y.spf_metric Copy_List_Items(y.collapsed_next_hops, y.next_hops) # Note that the Python heapq fucntion allows for duplicate items, # so we use the 'spf_visited' property to only consider a node # as min_node the first time it gets removed from the heap. def SPF_No_Traverse_Block_Root(topo, spf_root, block_root, direction): spf_heap = [] for y in topo.island_node_list: y.spf_metric = 2147483647 # 2^31-1 y.next_hops = [] y.spf_visited = False spf_root.spf_metric = 0 heapq.heappush(spf_heap, (spf_root.spf_metric, spf_root.node_id, spf_root) ) while spf_heap != []: #extract third element of tuple popped from heap min_node = heapq.heappop(spf_heap)[2] if min_node.spf_visited: continue min_node.spf_visited = True Store_Results(min_node, direction) if ( (min_node is spf_root) or (min_node is not block_root) ): for intf in min_node.island_intf_list: if ( ( (direction == 'INCREASING' and intf.OUTGOING ) or (direction == 'DECREASING' and intf.INCOMING ) ) and In_Common_Block(spf_root, intf.remote_node) ) : path_metric = min_node.spf_metric + intf.metric if path_metric < intf.remote_node.spf_metric: intf.remote_node.spf_metric = path_metric if min_node is spf_root: intf.remote_node.next_hops = [intf] else: Copy_List_Items(intf.remote_node.next_hops, min_node.next_hops) heapq.heappush(spf_heap, ( intf.remote_node.spf_metric, intf.remote_node.node_id, intf.remote_node ) ) elif path_metric == intf.remote_node.spf_metric: if min_node is spf_root: Add_Item_To_List_If_New( intf.remote_node.next_hops,intf) else: for nh_intf in min_node.next_hops: Add_Item_To_List_If_New( intf.remote_node.next_hops,nh_intf) def Normal_SPF(topo, spf_root): spf_heap = [] for y in topo.node_list: y.spf_metric = 2147483647 # 2^31-1 as max metric y.next_hops = [] y.primary_spf_metric = 2147483647 y.primary_next_hops = [] y.spf_visited = False spf_root.spf_metric = 0 heapq.heappush(spf_heap, (spf_root.spf_metric,spf_root.node_id,spf_root) ) while spf_heap != []: #extract third element of tuple popped from heap min_node = heapq.heappop(spf_heap)[2] if min_node.spf_visited: continue min_node.spf_visited = True Store_Results(min_node, 'NORMAL_SPF') for intf in min_node.intf_list: path_metric = min_node.spf_metric + intf.metric if path_metric < intf.remote_node.spf_metric: intf.remote_node.spf_metric = path_metric if min_node is spf_root: intf.remote_node.next_hops = [intf] else: Copy_List_Items(intf.remote_node.next_hops, min_node.next_hops) heapq.heappush(spf_heap, ( intf.remote_node.spf_metric, intf.remote_node.node_id, intf.remote_node ) ) elif path_metric == intf.remote_node.spf_metric: if min_node is spf_root: Add_Item_To_List_If_New( intf.remote_node.next_hops,intf) else: for nh_intf in min_node.next_hops: Add_Item_To_List_If_New( intf.remote_node.next_hops,nh_intf) def Set_Edge(y): if (y.blue_next_hops == [] and y.red_next_hops == []): Set_Edge(y.localroot) Copy_List_Items(y.blue_next_hops,y.localroot.blue_next_hops) Copy_List_Items(y.red_next_hops ,y.localroot.red_next_hops) y.order_proxy = y.localroot.order_proxy def Compute_MRT_NH_For_One_Src_To_Island_Dests(topo,x): for y in topo.island_node_list: y.HIGHER = False y.LOWER = False y.red_next_hops = [] y.blue_next_hops = [] y.order_proxy = y SPF_No_Traverse_Block_Root(topo, x, x.localroot, 'INCREASING') SPF_No_Traverse_Block_Root(topo, x, x.localroot, 'DECREASING') for y in topo.island_node_list: if ( y is not x and (y.block_id == x.block_id) ): assert (not ( y is x.localroot or x is y.localroot) ) assert(not (y.HIGHER and y.LOWER) ) if y.HIGHER == True: Copy_List_Items(y.red_next_hops, x.localroot.red_next_hops) elif y.LOWER == True: Copy_List_Items(y.blue_next_hops, x.localroot.blue_next_hops) else: Copy_List_Items(y.blue_next_hops, x.localroot.red_next_hops) Copy_List_Items(y.red_next_hops, x.localroot.blue_next_hops) # Inherit x's MRT next-hops to reach the GADAG root # from x's MRT next-hops to reach its local root, # but first check if x is the gadag_root (in which case # x does not have a local root) or if x's local root # is the gadag root (in which case we already have the # x's MRT next-hops to reach the gadag root) if x is not topo.gadag_root and x.localroot is not topo.gadag_root: Copy_List_Items(topo.gadag_root.blue_next_hops, x.localroot.blue_next_hops) Copy_List_Items(topo.gadag_root.red_next_hops, x.localroot.red_next_hops) topo.gadag_root.order_proxy = x.localroot # Inherit next-hops and order_proxies to other blocks for y in topo.island_node_list: if (y is not topo.gadag_root and y is not x ): Set_Edge(y) def Store_MRT_Nexthops_For_One_Src_To_Island_Dests(topo,x): for y in topo.island_node_list: if y is x: continue x.blue_next_hops_dict[y.node_id] = [] x.red_next_hops_dict[y.node_id] = [] Copy_List_Items(x.blue_next_hops_dict[y.node_id], y.blue_next_hops) Copy_List_Items(x.red_next_hops_dict[y.node_id], y.red_next_hops) def Store_Primary_and_Alts_For_One_Src_To_Island_Dests(topo,x): for y in topo.island_node_list: x.pnh_dict[y.node_id] = [] Copy_List_Items(x.pnh_dict[y.node_id], y.primary_next_hops) x.alt_dict[y.node_id] = [] Copy_List_Items(x.alt_dict[y.node_id], y.alt_list) def Store_MRT_NHs_For_One_Src_To_Named_Proxy_Nodes(topo,x): for prefix in topo.named_proxy_dict: P = topo.named_proxy_dict[prefix] x.blue_next_hops_dict[P.node_id] = [] x.red_next_hops_dict[P.node_id] = [] Copy_List_Items(x.blue_next_hops_dict[P.node_id], P.blue_next_hops) Copy_List_Items(x.red_next_hops_dict[P.node_id], P.red_next_hops) if P.convert_blue_to_green: x.blue_to_green_nh_dict[P.node_id] = True if P.convert_red_to_green: x.red_to_green_nh_dict[P.node_id] = True def Store_Alts_For_One_Src_To_Named_Proxy_Nodes(topo,x): for prefix in topo.named_proxy_dict: P = topo.named_proxy_dict[prefix] x.alt_dict[P.node_id] = [] Copy_List_Items(x.alt_dict[P.node_id], P.alt_list) def Store_Primary_NHs_For_One_Source_To_Nodes(topo,x): for y in topo.node_list: x.pnh_dict[y.node_id] = [] Copy_List_Items(x.pnh_dict[y.node_id], y.primary_next_hops) def Store_Primary_NHs_For_One_Src_To_Named_Proxy_Nodes(topo,x): for prefix in topo.named_proxy_dict: P = topo.named_proxy_dict[prefix] x.pnh_dict[P.node_id] = [] Copy_List_Items(x.pnh_dict[P.node_id], P.primary_next_hops) def Select_Alternates_Internal(D, F, primary_intf, D_lower, D_higher, D_topo_order): if D_higher and D_lower: if F.HIGHER and F.LOWER: if F.topo_order > D_topo_order: return 'USE_BLUE' else: return 'USE_RED' if F.HIGHER: return 'USE_RED' if F.LOWER: return 'USE_BLUE' assert(False) if D_higher: if F.HIGHER and F.LOWER: return 'USE_BLUE' if F.LOWER: return 'USE_BLUE' if F.HIGHER: if (F.topo_order > D_topo_order): return 'USE_BLUE' if (F.topo_order < D_topo_order): return 'USE_RED' assert(False) assert(False) if D_lower: if F.HIGHER and F.LOWER: return 'USE_RED' if F.HIGHER: return 'USE_RED' if F.LOWER: if F.topo_order > D_topo_order: return 'USE_BLUE' if F.topo_order < D_topo_order: return 'USE_RED' assert(False) assert(False) else: # D is unordered wrt S if F.HIGHER and F.LOWER: if primary_intf.OUTGOING and primary_intf.INCOMING: assert(False) if primary_intf.OUTGOING: # this case isn't hit it topo-9e return 'USE_BLUE' if primary_intf.INCOMING: return 'USE_RED' assert(False) if F.LOWER: return 'USE_RED' if F.HIGHER: return 'USE_BLUE' assert(False) def Select_Alternates(D, F, primary_intf): if (D is F) or (D.order_proxy is F): return 'PRIM_NH_IS_D_OR_OP_FOR_D' D_lower = D.order_proxy.LOWER D_higher = D.order_proxy.HIGHER D_topo_order = D.order_proxy.topo_order return Select_Alternates_Internal(D, F, primary_intf, D_lower, D_higher, D_topo_order) def Select_Alts_For_One_Src_To_Island_Dests(topo,x): Normal_SPF(topo, x) for D in topo.island_node_list: D.alt_list = [] if D is x: continue for primary_intf in D.primary_next_hops: alt = Alternate() alt.failed_intf = primary_intf if primary_intf in x.island_intf_list: alt.info = Select_Alternates(D, primary_intf.remote_node, primary_intf) else: alt.info = 'PRIM_NH_NOT_IN_ISLAND' Copy_List_Items(alt.nh_list, D.blue_next_hops) alt.fec = 'BLUE' alt.prot = 'NODE_PROTECTION' if (alt.info == 'USE_BLUE'): Copy_List_Items(alt.nh_list, D.blue_next_hops) alt.fec = 'BLUE' alt.prot = 'NODE_PROTECTION' if (alt.info == 'USE_RED'): Copy_List_Items(alt.nh_list, D.red_next_hops) alt.fec = 'RED' alt.prot = 'NODE_PROTECTION' if (alt.info == 'PRIM_NH_IS_D_OR_OP_FOR_D'): if primary_intf.OUTGOING and primary_intf.INCOMING: # cut-link: if there are parallel cut links, use # the link(s) with lowest metric that are not # primary intf or None cand_alt_list = [None] min_metric = 2147483647 for intf in x.island_intf_list: if ( intf is not primary_intf and (intf.remote_node is primary_intf.remote_node)): if intf.metric < min_metric: cand_alt_list = [intf] min_metric = intf.metric elif intf.metric == min_metric: cand_alt_list.append(intf) if cand_alt_list != [None]: alt.fec = 'GREEN' alt.prot = 'PARALLEL_CUTLINK' else: alt.fec = 'NO_ALTERNATE' alt.prot = 'NO_PROTECTION' Copy_List_Items(alt.nh_list, cand_alt_list) elif primary_intf in D.red_next_hops: Copy_List_Items(alt.nh_list, D.blue_next_hops) alt.fec = 'BLUE' alt.prot = 'LINK_PROTECTION' else: Copy_List_Items(alt.nh_list, D.red_next_hops) alt.fec = 'RED' alt.prot = 'LINK_PROTECTION' D.alt_list.append(alt) def Write_GADAG_To_File(topo, file_prefix): gadag_edge_list = [] for node in topo.island_node_list: for intf in node.island_intf_list: if intf.OUTGOING: local_node = "%04d" % (intf.local_node.node_id) remote_node = "%04d" % (intf.remote_node.node_id) intf_data = "%03d" % (intf.link_data) edge_string=(local_node+','+remote_node+','+ intf_data+'\n') gadag_edge_list.append(edge_string) gadag_edge_list.sort(); filename = file_prefix + '_gadag.csv' with open(filename, 'w') as gadag_file: gadag_file.write('local_node,'\ 'remote_node,local_intf_link_data\n') for edge_string in gadag_edge_list: gadag_file.write(edge_string); def Write_MRTs_For_All_Dests_To_File(topo, color, file_prefix): edge_list = [] for node in topo.island_node_list: if color == 'blue': node_next_hops_dict = node.blue_next_hops_dict elif color == 'red': node_next_hops_dict = node.red_next_hops_dict for dest_node_id in node_next_hops_dict: for intf in node_next_hops_dict[dest_node_id]: gadag_root = "%04d" % (topo.gadag_root.node_id) dest_node = "%04d" % (dest_node_id) local_node = "%04d" % (intf.local_node.node_id) remote_node = "%04d" % (intf.remote_node.node_id) intf_data = "%03d" % (intf.link_data) edge_string=(gadag_root+','+dest_node+','+local_node+ ','+remote_node+','+intf_data+'\n') edge_list.append(edge_string) edge_list.sort() filename = file_prefix + '_' + color + '_to_all.csv' with open(filename, 'w') as mrt_file: mrt_file.write('gadag_root,dest,'\ 'local_node,remote_node,link_data\n') for edge_string in edge_list: mrt_file.write(edge_string); def Write_Both_MRTs_For_All_Dests_To_File(topo, file_prefix): Write_MRTs_For_All_Dests_To_File(topo, 'blue', file_prefix) Write_MRTs_For_All_Dests_To_File(topo, 'red', file_prefix) def Write_Alternates_For_All_Dests_To_File(topo, file_prefix): edge_list = [] for x in topo.island_node_list: for dest_node_id in x.alt_dict: alt_list = x.alt_dict[dest_node_id] for alt in alt_list: for alt_intf in alt.nh_list: gadag_root = "%04d" % (topo.gadag_root.node_id) dest_node = "%04d" % (dest_node_id) prim_local_node = \ "%04d" % (alt.failed_intf.local_node.node_id) prim_remote_node = \ "%04d" % (alt.failed_intf.remote_node.node_id) prim_intf_data = \ "%03d" % (alt.failed_intf.link_data) if alt_intf == None: alt_local_node = "None" alt_remote_node = "None" alt_intf_data = "None" else: alt_local_node = \ "%04d" % (alt_intf.local_node.node_id) alt_remote_node = \ "%04d" % (alt_intf.remote_node.node_id) alt_intf_data = \ "%03d" % (alt_intf.link_data) edge_string = (gadag_root+','+dest_node+','+ prim_local_node+','+prim_remote_node+','+ prim_intf_data+','+alt_local_node+','+ alt_remote_node+','+alt_intf_data+','+ alt.fec +'\n') edge_list.append(edge_string) edge_list.sort() filename = file_prefix + '_alts_to_all.csv' with open(filename, 'w') as alt_file: alt_file.write('gadag_root,dest,'\ 'prim_nh.local_node,prim_nh.remote_node,'\ 'prim_nh.link_data,alt_nh.local_node,'\ 'alt_nh.remote_node,alt_nh.link_data,'\ 'alt_nh.fec\n') for edge_string in edge_list: alt_file.write(edge_string); def Raise_GADAG_Root_Selection_Priority(topo,node_id): node = topo.node_dict[node_id] node.GR_sel_priority = 255 def Lower_GADAG_Root_Selection_Priority(topo,node_id): node = topo.node_dict[node_id] node.GR_sel_priority = 128 def GADAG_Root_Compare(node_a, node_b): if (node_a.GR_sel_priority > node_b.GR_sel_priority): return 1 elif (node_a.GR_sel_priority < node_b.GR_sel_priority): return -1 else: if node_a.node_id > node_b.node_id: return 1 elif node_a.node_id < node_b.node_id: return -1 def Set_GADAG_Root(topo,computing_router): gadag_root_list = [] for node in topo.island_node_list: gadag_root_list.append(node) gadag_root_list.sort(GADAG_Root_Compare) topo.gadag_root = gadag_root_list.pop() def Run_MRT_for_One_Source(topo, src): Reset_Computed_Node_and_Intf_Values(topo) MRT_Island_Identification(topo, src, 0, 0) Set_Island_Intf_and_Node_Lists(topo) Set_GADAG_Root(topo,src) Sort_Interfaces(topo) Run_Lowpoint(topo) Assign_Remaining_Lowpoint_Parents(topo) Construct_GADAG_via_Lowpoint(topo) Run_Assign_Block_ID(topo) Add_Undirected_Links(topo) Compute_MRT_NH_For_One_Src_To_Island_Dests(topo,src) Store_MRT_Nexthops_For_One_Src_To_Island_Dests(topo,src) Select_Alts_For_One_Src_To_Island_Dests(topo,src) Store_Primary_and_Alts_For_One_Src_To_Island_Dests(topo,src) def Run_Prim_SPF_for_One_Source(topo,src): Normal_SPF(topo, src) Store_Primary_NHs_For_One_Source_To_Nodes(topo,src) def Run_MRT_for_All_Sources(topo): for src in topo.node_list: if 0 in src.profile_id_list: # src runs MRT if it has profile_id=0 Run_MRT_for_One_Source(topo,src) else: # still run SPF for nodes not running MRT Run_Prim_SPF_for_One_Source(topo,src) def Write_Output_To_Files(topo,file_prefix): Write_GADAG_To_File(topo,file_prefix) Write_Both_MRTs_For_All_Dests_To_File(topo,file_prefix) Write_Alternates_For_All_Dests_To_File(topo,file_prefix) def Create_Example_Topology_Input_File(filename): data = [[01,02,10],[02,03,10],[03,04,11],[04,05,10,20],[05,06,10], [06,07,10],[06,07,10],[06,07,15],[07,01,10],[07,51,10], [51,52,10],[52,53,10],[53,03,10],[01,55,10],[55,06,10], [04,12,10],[12,13,10],[13,14,10],[14,15,10],[15,16,10], [16,17,10],[17,04,10],[05,76,10],[76,77,10],[77,78,10], [78,79,10],[79,77,10]] with open(filename, 'w') as topo_file: for item in data: if len(item) > 3: line = (str(item[0])+','+str(item[1])+','+ str(item[2])+','+str(item[3])+'\n') else: line = (str(item[0])+','+str(item[1])+','+ str(item[2])+'\n') topo_file.write(line) def Generate_Example_Topology_and_Run_MRT(): Create_Example_Topology_Input_File('example_topo_input_file.csv') topo = Create_Topology_From_File('example_topo_input_file.csv') res_file_base = 'example_topo' Raise_GADAG_Root_Selection_Priority(topo,3) Run_MRT_for_All_Sources(topo) Write_Output_To_Files(topo, res_file_base) Generate_Example_Topology_and_Run_MRT() ]]>
This specification defines the MRT Lowpoint Algorithm, which is one option among several possible MRT algorithms. Other alternatives are described in the appendices. In addition, it is possible to calculate Destination-Rooted GADAG, where for each destination, a GADAG rooted at that destination is computed. Then a router can compute the blue MRT and red MRT next-hops to that destination. Building GADAGs per destination is computationally more expensive, but may give somewhat shorter alternate paths. It may be useful for live-live multicast along MRTs.
The MRT Lowpoint algorithm is the lowest computation of the MRT algorithms. Two other MRT algorithms are provided in and . When analyzed on service provider network topologies, they did not provide significant differences in the path lenghts for the alternatives. This section does not focus on that analysis or the decision to use the MRT Lowpoint algorithm as the default MRT algorithm; it has the lowest computational and storage requirements and gave comparable results. Since this document defines the MRT Lowpoint algorithm for use in fast-reroute applications, it is useful to compare MRT and Remote LFA . This section compares MRT and remote LFA for IP Fast Reroute in 19 service provider network topologies, focusing on coverage and alternate path length. shows the node-protecting coverage provided by local LFA (LLFA), remote LFA (RLFA), and MRT against different failure scenarios in these topologies. The coverage values are calculated as the percentage of source-destination pairs protected by the given IPFRR method relative to those protectable by optimal routing, against the same failure modes. More details on alternate selection policies used for this analysis are described later in this section.
For the topologies analyzed here, LLFA is able to provide node-protecting coverage ranging from 37% to 95% of the source-destination pairs, as seen in the column labeled NP_LLFA. The use of RLFA in addition to LLFA is generally able to increase the node-protecting coverage. The percentage of node-protecting coverage with RLFA is provided in the column labeled NP_RLFA, ranges from 59% to 98% for these topologies. The node-protecting coverage provided by MRT is 100% since MRT is able to provide protection for any source-destination pair for which a path still exists after the failure. We would also like to measure the quality of the alternate paths produced by these different IPFRR methods. An obvious approach is to take an average of the alternate path costs over all source-destination pairs and failure modes. However, this presents a problem, which we will illustrate by presenting an example of results for one topology using this approach ( ). In this table, the average relative path length is the alternate path length for the IPFRR method divided by the optimal alternate path length, averaged over all source-destination pairs and failure modes. The first three columns of data in the table give the path length calculated from the sum of IGP metrics of the links in the path. The results for topology T208 show that the metric-based path lengths for NP_LLFA and NP_RLFA alternates are on average 78 and 66 times longer than the path lengths for optimal alternates. The metric-based path lengths for MRT alternates are on average 14 times longer than for optimal alternates.
The network topology represented by T208 uses values of 10, 100, and 1000 as IGP costs, so small deviations from the optimal alternate path can result in large differences in relative path length. LLFA, RLFA, and MRT all allow for at least one hop in the alterate path to be chosen independent of the cost of the link. This can easily result in an alternate using a link with cost 1000, which introduces noise into the path length measurement. In the case of T208, the adverse effects of using metric-based path lengths is obvious. However, we have observed that the metric-based path length introduces noise into alternate path length measurements in several other topologies as well. For this reason, we have opted to measure the alternate path length using hopcount. While IGP metrics may be adjusted by the network operator for a number of reasons (e.g. traffic engineering), the hopcount is a fairly stable measurement of path length. As shown in the last three columns of , the hopcount-based alternate path lengths for topology T208 are fairly well-behaved. , , , and present the hopcount-based path length results for the 19 topologies examined. The topologies in the four tables are grouped based on the size of the topologies, as measured by the number of nodes, with having the smallest topologies and having the largest topologies. Instead of trying to represent the path lengths of a large set of alternates with a single number, we have chosen to present a histogram of the path lengths for each IPFRR method and alternate selection policy studied. The first eight colums of data represent the percentage of failure scenarios protected by an alternate N hops longer than the primary path, with the first column representing an alternate 0 or 1 hops longer than the primary path, all the way up through the eighth column respresenting an alternate 14 or 15 hops longer than the primary path. The last column in the table gives the percentage of failure scenarios for which there is no alternate less than 16 hops longer than the primary path. In the case of LLFA and RLFA, this category includes failure scenarios for which no alternate was found. For each topology, the first row (labeled OPTIMAL) is the distribution of the number of hops in excess of the primary path hopcount for optimally routed alternates. (The optimal routing was done with respect to IGP metrics, as opposed to hopcount.) The second row(labeled NP_LLFA) is the distribution of the extra hops for node-protecting LLFA. The third row (labeled NP_LLFA_THEN_NP_RLFA) is the hopcount distribution when one adds node-protecting RLFA to increase the coverage. The alternate selection policy used here first tries to find a node-protecting LLFA. If that does not exist, then it tries to find an RLFA, and checks if it is node-protecting. Comparing the hopcount distribution for RLFA and LLFA across these topologies, one can see how the coverage is increased at the expense of using longer alternates. It is also worth noting that while superficially LLFA and RLFA appear to have better hopcount distributions than OPTIMAL, the presence of entries in the last column (no alternate < 16) mainly represent failure scenarios that are not protected, for which the hopcount is effectively infinite. The fourth and fifth rows of each topology show the hopcount distributions for two alternate selection policies using MRT alternates. The policy represented by the label NP_LLFA_THEN_MRT_LOWPOINT will first use a node-protecting LLFA. If a node-protecting LLFA does not exist, then it will use an MRT alternate. The policy represented by the label MRT_LOWPOINT instead will use the MRT alternate even if a node-protecting LLFA exists. One can see from the data that combining node-protecting LLFA with MRT results in a significant shortening of the alternate hopcount distribution.
In the preceding analysis, the following procedure for selecting an RLFA was used. Nodes were ordered with respect to distance from the source and checked for membership in Q and P-space. The first node to satisfy this condition was selected as the RLFA. More sophisticated methods to select node-protecting RLFAs is an area of active research. The analysis presented above uses the MRT Lowpoint Algorithm defined in this specification with a common GADAG root. The particular choice of a common GADAG root is expected to affect the quality of the MRT alternate paths, with a more central common GADAG root resulting in shorter MRT alternate path lengths. For the analysis above, the GADAG root was chosen for each topology by calculating node centrality as the sum of costs of all shortest paths to and from a given node. The node with the lowest sum was chosen as the common GADAG root. In actual deployments, the common GADAG root would be chosen based on the GADAG Root Selection Priority advertised by each router, the values of which would be determined off-line. In order to measure how sensitive the MRT alternate path lengths are to the choice of common GADAG root, we performed the same analysis using different choices of GADAG root. All of the nodes in the network were ordered with respect to the node centrality as computed above. Nodes were chosen at the 0th, 25th, and 50th percentile with respect to the centrality ordering, with 0th percentile being the most central node. The distribution of alternate path lengths for those three choices of GADAG root are shown in for a subset of the 19 topologies (chosen arbitrarily). The third row for each topology (labeled MRT_LOWPOINT ( 0 percentile) ) reproduces the results presented above for MRT_LOWPOINT_ONLY. The fourth and fifth rows show the alternate path length distibution for the 25th and 50th percentile choice for GADAG root. One can see some impact on the path length distribution with the less central choice of GADAG root resulting in longer path lenghths. We also looked at the impact of MRT algorithm variant on the alternate path lengths. The first two rows for each topology present results of the same alternate path length distribution analysis for the SPF and Hybrid methods for computing the GADAG. These two methods are described in and . For three of the topologies in this subset (T201, T206, and T211), the use of SPF or Hybrid methods does not appear to provide a significant advantage over the Lowpoint method with respect to path length. Instead, the choice of GADAG root appears to have more impact on the path length. However, for two of the topologies in this subset(T216 and T219) and for this particular choice of GAGAG root, the use of the SPF method results in noticeably shorter alternate path lengths than the use of the Lowpoint or Hybrid methods. It remains to be determined if this effect applies generally across more topologies or is sensitive to choice of GADAG root.
[RFC Editor: please remove this section prior to publication.] Please see for details on implementation status.
The algorithm assumes that broadcast interfaces are already represented as pseudo-nodes in the network graph. Given maximal redundancy, one of the MRT will try to avoid both the pseudo-node and the next hop. The exact rules need to be fully specified.
The authors would like to thank Shraddha Hegde for her suggestions and review.
This document includes no request to IANA.
This architecture is not currently believed to introduce new security concerns.
&RFC2119; &RFC3137; &RFC5120; &RFC5286; &RFC5714; &RFC7490; &I-D.ietf-rtgwg-ipfrr-notvia-addresses; &I-D.ietf-rtgwg-lfa-manageability; Topological sorting of large networks Novel Algorithms for IP Fast Reroute &RFC6571; IP Fast ReRoute: Lightweight Not-Via without Additional Addresses IP Fast ReRoute: Loop Free Alternates Revisited On Finding Maximally Redundant Trees in Strictly Linear Time
The basic idea in this option is to use slightly-modified SPF computations to find ears. In every block, an SPF computation is first done to find a cycle from the local root and then SPF computations in that block find ears until there are no more interfaces to be explored. The used result from the SPF computation is the path of interfaces indicated by following the previous hops from the mininized IN_GADAG node back to the SPF root. To do this, first all cut-vertices must be identified and local-roots assigned as specified in . The slight modifications to the SPF are as follows. The root of the block is referred to as the block-root; it is either the GADAG root or a cut-vertex. The SPF is rooted at a neighbor x of an IN_GADAG node y. All links between y and x are marked as TEMP_UNUSABLE. They should not be used during the SPF computation. If y is not the block-root, then it is marked TEMP_UNUSABLE. It should not be used during the SPF computation. This prevents ears from starting and ending at the same node and avoids cycles; the exception is because cycles to/from the block-root are acceptable and expected. Do not explore links to nodes whose local-root is not the block-root. This keeps the SPF confined to the particular block. Terminate when the first IN_GADAG node z is minimized. Respect the existing directions (e.g. INCOMING, OUTGOING, UNDIRECTED) already specified for each interface.
Assume that an ear is found by going from y to x and then running an SPF that terminates by minimizing z (e.g. y<->x...q<->z). Now it is necessary to determine the direction of the ear; if y << z, then the path should be y->x...q->z but if y >> z, then the path should be y<-x...q<-z. In , the same problem was handled by finding all ears that started at a node before looking at ears starting at nodes higher in the partial order. In this algorithm, using that approach could mean that new ears aren't added in order of their total cost since all ears connected to a node would need to be found before additional nodes could be found. The alternative is to track the order relationship of each node with respect to every other node. This can be accomplished by maintaining two sets of nodes at each node. The first set, Higher_Nodes, contains all nodes that are known to be ordered above the node. The second set, Lower_Nodes, contains all nodes that are known to be ordered below the node. This is the approach used in this algorithm.
A goal of the algorithm is to find the shortest cycles and ears. An ear is started by going to a neighbor x of an IN_GADAG node y. The path from x to an IN_GADAG node is minimal, since it is computed via SPF. Since a shortest path is made of shortest paths, to find the shortest ears requires reaching from the set of IN_GADAG nodes to the closest node that isn't IN_GADAG. Therefore, an ordered tree is maintained of interfaces that could be explored from the IN_GADAG nodes. The interfaces are ordered by their characteristics of metric, local loopback address, remote loopback address, and ifindex, as in the algorithm previously described in . The algorithm ignores interfaces picked from the ordered tree that belong to the block root if the block in which the interface is present already has an ear that has been computed. This is necessary since we allow at most one incoming interface to a block root in each block. This requirement stems from the way next-hops are computed as was seen in . After any ear gets computed, we traverse the newly added nodes to the GADAG and insert interfaces whose far end is not yet on the GADAG to the ordered tree for later processing. Finally, cut-links are a special case because there is no point in doing an SPF on a block of 2 nodes. The algorithm identifies cut-links simply as links where both ends of the link are cut-vertices. Cut-links can simply be added to the GADAG with both OUTGOING and INCOMING specified on their interfaces.
In this option, the idea is to combine the salient features of the lowpoint inheritance and SPF methods. To this end, we process nodes as they get added to the GADAG just like in the lowpoint inheritance by maintaining a stack of nodes. This ensures that we do not need to maintain lower and higher sets at each node to ascertain ear directions since the ears will always be directed from the node being processed towards the end of the ear. To compute the ear however, we resort to an SPF to have the possibility of better ears (path lentghs) thus giving more flexibility than the restricted use of lowpoint/dfs parents. Regarding ears involving a block root, unlike the SPF method which ignored interfaces of the block root after the first ear, in the hybrid method we would have to process all interfaces of the block root before moving on to other nodes in the block since the direction of an ear is pre-determined. Thus, whenever the block already has an ear computed, and we are processing an interface of the block root, we mark the block root as unusable before the SPF run that computes the ear. This ensures that the SPF terminates at some node other than the block-root. This in turn guarantees that the block-root has only one incoming interface in each block, which is necessary for correctly computing the next-hops on the GADAG. As in the SPF gadag, bridge ears are handled as a special case. The entire algorithm is shown below in