# 9.6: Link-State Routing-Update Algorithm

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Link-state routing is an alternative to distance-vector. It is often – though certainly not always – considered to be the routing-update algorithm class of choice for networks that are “sufficiently large”, such as those of ISPs. There are two specific link-state protocols: the IETF’s Open Shortest Path First (OSPF, RFC 2328), and OSI’s Intermediate Systems to Intermediate Systems (IS-IS, documented unofficially in RFC 1142).

In distance-vector routing, each node knows a bare minimum of network topology: it knows nothing about links beyond those to its immediate neighbors. In the link-state approach, each node keeps a maximum amount of network information: a full map of all nodes and all links. Routes are then computed locally from this map, using the shortest-path-first algorithm. The existence of this map allows, in theory, the calculation of different routes for different quality-of-service requirements. The map also allows calculation of a new route as soon as news of the failure of the existing route arrives; distance-vector protocols on the other hand must wait for news of a new route after an existing route fails.

It is possible for ephemeral routing loops to exist; for example, if one router has received a LSP but another has not, they may have an inconsistent view of the network and thus route to one another. However, as soon as the LSP has reached all routers involved, the loop should vanish. There are no “race conditions”, as with distance-vector routing, that can lead to persistent routing loops.

The link-state flooding algorithm avoids the usual problems of broadcast in the presence of loops by having each node keep a database of all LSP messages. The originator of each LSP includes its identity, information about the link that has changed status, and also a sequence number. Other routers need only keep in their databases the LSP packet with the largest sequence number; older LSPs can be discarded. When a router receives a LSP, it first checks its database to see if that LSP is old, or is current but has been received before; in these cases, no further action is taken. If, however, an LSP arrives with a sequence number not seen before, then in typical broadcast fashion the LSP is retransmitted over all links except the arrival interface.

As an example, consider the following arrangement of routers:

Suppose the A–E link status changes. A sends LSPs to C and B. Both these will forward the LSPs to D; suppose B’s arrives first. Then D will forward the LSP to C; the LSP traveling C→D and the LSP traveling D→C might even cross on the wire. D will ignore the second LSP copy that it receives from C and C will ignore the second copy it receives from D.

It is important that LSP sequence numbers not wrap around. (Protocols that do allow a numeric field to wrap around usually have a clear-cut idea of the “active range” that can be used to conclude that the numbering has wrapped rather than restarted; this is harder to do in the link-state context.) OSPF uses lollipop sequence-numbering here: sequence numbers begin at -231 and increment to 231-1. At this point they wrap around back to 0. Thus, as long as a sequence number is less than zero, it is guaranteed unique; at the same time, routing will not cease if more than 231 updates are needed. Other link-state implementations use 64-bit sequence numbers.

Actual link-state implementations often give link-state records a maximum lifetime; entries must be periodically renewed.

• ## 9.5.1 Shortest-Path-First Algorithm

The next step is to compute routes from the network map, using the shortest-path-first (SPF) algorithm. This algorithm computes shortest paths from a given node, A in the example here, to all other nodes. Below is our example network; we are interested in the shortest paths from A to B, C and D.

Before starting the algorithm, we note the shortest path from A to D is A-B-C-D, which has cost 3+4+2=9.

The algorithm builds the set R of all shortest-path routes iteratively. Initially, R contains only the 0-length route to the start node; one new destination and route is added to R at each stage of the iteration. At each stage we have a current node, representing the node most recently added to R. The initial current node is our starting node, in this case, A.

We will also maintain a set T, for tentative, of routes to other destinations. This is also initialized to empty.

At each stage, we find all nodes which are immediate neighbors of the current node and which do not already have routes in the set R. For each such node N, we calculate the cost of the route from the start node to N that goes through the current node. We see if this is our first route to N, or if the route improves on any route to N already in T; if so, we add or update the route in T accordingly. Doing this, the routes will be discovered in order of increasing (or nondecreasing) cost.

At the end of this process, we choose the shortest path in T, and move the route and destination node to R. The destination node of this shortest path becomes the next current node. Ties can be resolved arbitrarily, but note that, as with distance-vector routing, we must choose the minimum or else the accurate-costs property will fail.

We repeat this process until all nodes have routes in the set R.

For the example above, we start with current = A and R = {⟨A,A,0⟩}. The set T will be {⟨B,B,3⟩, ⟨C,C,10⟩, ⟨D,D,11⟩}. The lowest-cost entry is ⟨B,B,3⟩, so we move that to R and continue with current = B. No path through C or D can possibly have lower cost.

For the next stage, the neighbors of B without routes in R are C and D; the routes from A to these through B are ⟨C,B,7⟩ and ⟨D,B,12⟩. The former is an improvement on the existing T entry ⟨C,C,10⟩ and so replaces it; the latter is not an improvement over ⟨D,D,11⟩. T is now {⟨C,B,7⟩, ⟨D,D,11⟩}. The lowest-cost route in T is that to C, so we move this node and route to R and set C to be current.

Again, ⟨C,B,7⟩ must be the shortest path to C. If any lower-cost path to C existed, then we would be selecting that shorter path – or a prefix of it – at this point, instead of the ⟨C,B,7⟩ path; see the proof below.

For the next stage, D is the only non-R neighbor; the path from A to D via C has entry ⟨D,B,9⟩, an improvement over the existing ⟨D,D,11⟩ in T. The only entry in T is now ⟨D,B,9⟩; this has the lowest cost and thus we move it to R.

We now have routes in R to all nodes, and are done.

Here is another example, again with links labeled with costs:

We start with current = A. At the end of the first stage, ⟨B,B,3⟩ is moved into R, T is {⟨D,D,12⟩}, and current is B. The second stage adds ⟨C,B,5⟩ to T, and then moves this to R; current then becomes C. The third stage introduces the route (from A) ⟨D,B,10⟩; this is an improvement over ⟨D,D,12⟩ and so replaces it in T; at the end of the stage this route to D is moved to R.

In both the examples above, the current nodes progressed along a path, A→B→C→D. This is not generally the case; here is a similar example but with different lengths in which current jumps from B to D:

As in the previous example, at the end of the first stage ⟨B,B,3⟩ is moved into R, with T = {⟨D,D,4⟩}, and B becomes current. The second stage adds ⟨C,B,6⟩ to T. However, the shortest path in T is now ⟨D,D,4⟩, and so it is D that becomes the next current. The final stage replaces ⟨C,B,6⟩ in T with ⟨C,D,5⟩. At that point this route is added to R and the algorithm is completed.

Proof that SPF paths are shortest: suppose, by contradiction, that, for some node, a shorter path exists than the one generated by SPF. Let A be the start node, and let U be the first node generated for which the SPF path is not shortest. Let T be the Tentative set and let R be the set of completed routes at the point when we choose U as current, and let d be the cost of the new route to U. Let P = ⟨A,…,X,Y,…,U⟩ be the shorter path to U, with cost c<d, where Y is the first node along the path not to have a route in R (it is possible Y=U).
At some strictly earlier stage in the algorithm, we must have added a route to node X, as the route to X is in R. In the following stage, we would have included the prefix ⟨A,…,X,Y⟩ of P in Tentative. This path to Y has cost ≤c. This route must still be in T at the point we chose U as current, as there is no route to Y in R, but this means we should instead have chosen Y as current, contradicting the choice of U.

A link-state source node S computes the entire path to a destination D (in fact it computes the path to every destination). But as far as the actual path that a packet sent by S will take to D, S has direct control only as far as the first hop N. While the accurate-cost rule we considered in distance-vector routing will still hold, the actual path taken by the packet may differ from the path computed at the source, in the presence of alternative paths of the same length. For example, S may calculate a path S–N–A–D, and yet a packet may take path S–N–B–D, so long as the N–A–D and N–B–D paths have the same length.

Link-state routing allows calculation of routes on demand (results are then cached), or larger-scale calculation. Link-state also allows routes calculated with quality-of-service taken into account, via straightforward extension of the algorithm above.

Because the starting node is fixed, the shortest-path-first algorithm can be classified as a single-source approach. If the goal is to compute the shortest paths between all pairs of nodes in a network, the Floyd-Warshall algorithm [en.Wikipedia.org/wiki/Floyd%...all_algorithm] is an alternative, with slightly better performance in networks with large numbers of links.

• 9.6: Link-State Routing-Update Algorithm is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.