Routing in Geneal Distance Vector Routing

Routing in Geneal Distance Vector Routing

COLE POLYTECHNIQUE FDRALE DE LAUSANNE Routing in General Distance Vector Routing Jean-Yves Le Boudec Fall 2009 1 Contents 1. Routing in General 2. Distance vector Bellman-Ford How it is used in practice 3. Protocols that implement Distance Vector RIP RIP v2 IGRP 4. More about routing in general 2 1. Introduction Why were routing protocols invented IP assumes routing tables are maintained at hosts and routers used by Packet Forwarding Routing = control method maintain routing tables automatically in routers At host routing tables are usually maintained by default rules plus ICMP redirect in old times: was done also by a routing protocol (RIP). Today, a host usually does not run any routing protocol Compare to: LANs connected by bridges operate at layer 2 like connectionless packet forwarders Q. How do they maintain routing information ? solution 3 Routing vs Packet Forwarding Packet Forwarding for every packet done in real time Routing computation of routing tables or data structures for unicast and multicast

normally only between routers non-real time: latency up to 2 minutes uses dedicated protocols (RIP, OSPF, EIGRP (Cisco) for unicast and DVMRP, M-OSPF, PIM) ICMP-redirect may alter routing tables, but only in hosts 4 Interior Routing Routing methods are of two types Inside an administrative domain = Interior Routing Between domains = Exterior Routing Problem solved by a routing protocol What a routing protocol does find reachable destinations find best paths towards destinations best in the sense of some metric in this module, best means along shortest path, for some additive metric (number of hops, delay) 5 Metrics Distance vector and link state find paths that minimize a metric Static metric - does not depend on the network state; for example: number of hops link capacity and static delay cost Dynamic metric- depend on the network state link load current delay see end of section 6 Simple Routing Methods How routing protocols work static configuration for toy networks only flooding each packet duplicated on each outgoing link; loops prevented by packet id or other mechanism ; duplicate packets may be received at destination simple and robust no need for routing tables robust - tolerates link or router failures optimal in some sense the first packet has found the shortest path to the destination costly

many duplicated packets little useful traffic used as an ingredient by mobile ad-hoc routing methods (AODV, OLSR) source routing source writes route into packet header router reads next hop from packet header, moves pointer route discovered by flooding 7 Source Routing A B 2.2.4 SA DA RI data A B 2.2.4 1 2 A 1 IS 3 IS 4 2 1 IS 2 3 3 A B 2.2.4 1 2 IS 4 B 3 Q. What are the routes that can be used from A to B ? solution A route is described by a sequence of port numbers

8 Route Discovery in Token Rings R3 B2 B1 A R2 B3 R5 R1 R4 B4 B6 R6 B B5 One All Route Broadcast packet is generated by A. This creates 5 different packets. 1. A-R1-B1-R2-B2-R3 2. A-R1-B1-R2-B3-R5-B6-R6 3. A-R1-B1-R2-B3-R5-B5-R4 4. A-R1-B4-R4-B5-R5-B3-R2-B2-R3 5. A-R1-B4-R4-B5-R5-B6-R6 2 of them reach B (numbers 2 and 5) route_1 = R1.B1.R2.B3.R5.B6.R6 route_2 = R1.B4.R4.B5.R5.B6.R6 9 In the 1980s, the token was invented as a competitor to Ethernet. Bridging is in theory independent of whether we use token ring or ethrnet, however in practice token ring LANs used source routing bridges instead of spanning tree bridges. Source routing bridges work as illustrated on the figure: Bridges and token rings have numbers. Think of a token ring as functionally the same as an Ethernet collision domain Assume A has a packet to send to B (here A and B are MAC addresses, but this works equally well with IP addresses). A needs to find a description of a route to B. To this end, A floods the network with an all-route-broadcast packet. The packet is generated by A and sent over ring R1. This packet has a special destination address that means all-route-broadcast. All bridges listen to all rings that they are attached to (this is their job as forwarding devices). When they see a packet with destination address all-route-broadcast, they forward the packet to all other rings they are attached to, except if the packet has already visited this ring (the packet contains in its header the list of rings and bridges that it has already visited). For example, the packet created by A is seen by B1 [resp. B4] who forwards a copy on R2 [resp. R4]. B2 and B3 see the packet on R2 and forward it to R5 and R3. Etc. At some point in time, B4 sees a packet on R4 put by B5, which contains as list of visits: A-R1B1-R2-B3-R5-B5-R4 (packet number 3). This packet contains R1 in its list, therefore B4 does not

forward it. This generates 5 packets in total (numbered 1 to 5 on the figure), 2 of them reach ring R6. When B sees any of them, it sends an acknowledgement to A. This ack is source routed, along the reverse route. A then receives two acks, each of them contains source route information that can be inverted by A. A now has two routes to B and can choose for example the shortest (in number of hops). DSR (Dynamic Source Routing) is a protocol for routing in ad-hoc networks that uses the same mechanism, but with IP addresses instead of MAC addresses. 10 Other Methods Distance vector (Bellman-Ford) routers only know their local state link metric and neighbor estimates interior routing protocols (RIP, IGRP) Link state knowledge of the global state topology database global optimization (Shortest Path First - Dijkstra) interior routing protocols (OSPF, PNNI (ATM)) Path vector no knowledge of the global state path: sequence of AS with attributes global optimization and policy routing exterior routing protocols (BGP) 11 2. Distance Vector What it does: Computes best paths to all destinations Fully distributed Using as only information the distances from self to all destinations How it works uses distributed Bellman-Ford see next slides Note: individual link cost is setup by network management We first describe the centralized Bellman-Ford algorithm. 12 The Centralized Bellman-Ford Algorithm What: Given a directed graph with links costs A(i,j), computes the best path from i to j for any couple (i,j). We assume A(i, j) > 0 and A(i,j) = when i and j are not connected.

How: Take for example j=1 and let p(i) be the cost of the best path from i to 1. Define pk(i) as the cost of the best path from i to 1 in at most k hops. Let p0(1) = 0, p0(i) = for i 1. (Bellman Ford, BF1) Theorem 1. If the network is fully connected, the algorithm stops at the latest for k=n and then pk(i)=p(i) for all i 2. The shortest path from i 1 to 1 is defined by pred(i) = Argminji [A(i,j) + p(j)]. Idea of Proof: pk(i) is the distance from i to 1 in at most k hops. Comment: recursion is equivalent to : p k(i) = min{ minji, j1 [A(i,j) + pk-1(j)] , A(i,1) } 13 Example Apply the theorem: write pk(i), pred(i) and draw the shortest paths to node 1. 3 1 5 6 3 1 2 3 4 1 1 1 solution 14 Impact of Initial Conditions Example: Q. does the algorithm converge to the shortest path with initial condition as shown ? 3 1 5 6 3 1

2 3 4 1 1 1 k\i 1 2 3 4 5 0 0 0 0 0 0 1 2 3 4 k\i 1 2 3 4 5 0 0 6 1 1 0 1 2 3 4 solution 15 Impact of Initial Condition Theorem The algorithm converges in a finite number of steps to the correct values for all initial conditions such that p0(1)=0 and for every node i that is connected to 1 If there is no path from i to 1, the algorithm lets p k(i) converge to 1 16 Proof We do the proof assuming all nodes are connected. 1. Let pk be the vector pk[i], i=2,. Let B be the mapping that transforms an array x[i]i=2into the array Bx defined for i 1 by Bx[i]=min j i, j 1[A(i,j) + x(j)] Let b be the array defined for i 1 by b[i]= A(i,1) The algorithm can be rewritten in vector form as (1) pk = B pk-1 b where is the pointwise minimum 2. Eq (1) is a min-plus linear equation and the operator B satisfies B(x y)= Bx By. Thus, Eq(1) can be solved using min-plus algebra into (2) pk = Bkp0 Bk-1b Bb b 3. Define the array e for i 1 by e[i]= . Let p0=e. Eq (2) becomes (3) pk = Bk-1b Bb b. Now we have the Bellman Ford algorithm with classical initial

conditions, thus, by Theorem 1: (4) for k n-1: Bk-1b Bb b = q where q[i] is the distance from i to 1. 4. We can rewrite Eq(2) for k n-1 as (5) pk = Bkp0 q 5. Bkp0[i] can be written as A[i,i1]+ A[i1,i2]+ + A[ik-1,ik]+ p[ik] thus (6) Bkp0[i] k a, where a is the minimum of all A[i,j]. Thus Bkp0[i] tends to when k grows. Thus for k large enough, Bkp0 is larger than q and can be ignored in Eq(5). In other words, for k large enough : (6) pk = q 17 Distributed Bellman Ford BF1 can be used in a centralized algorithm to compute p(i) i.e. find the shortest path. However, this is not its main interest, because there is a better algorithm (Dijkstra) that can be used in a centralized method But: it can be distributed, as follows. Distributed DistributedBellman-Ford Bellman-FordAlgorithm Algorithmv1, v1,BFD1 BFD1 every node, say i, maintains an estimate q(i) every node, say i, maintains an estimate q(i)of ofthe thedistance distancep(i) p(i)to to some fixed node 1; initial conditions are arbitrary but q(1)=0 at all some fixed node 1; initial conditions are arbitrary but q(1)=0 at all steps steps

from fromtime timeto totime, time,i isends sendsthe thenew newvalue valueq(i) q(i)to toall allits itsneighbours neighbours when node i receives a value q(j ) from any neighbour when node i receives a value q(j00) from any neighbourj0j,0,ititsets setsq(j q(j0)0) to tothe thereceived receivedvalue valueand andupdates updatesq(i) q(i)by byrecomputing recomputing eq eq(1) (1) q(i) (A(i,j)+q(j)) q(i):= :=min minj neighbour j neighbour (A(i,j)+q(j)) ififeq eq(1) (1)causes causesq(i)

q(i)to tobe bemodified, modified,pred(i) pred(i)isisset setto toaavalue valueof ofj jthat that achieves the min achieves the min Theorem: if the time to reliably send a message is bounded by T, the algo converges to the same result as the centralized version in at most nT time units (if the network is fully connected) 18 Distributed Bellman-Ford v1 A possible run of algorithm v1. The table shows the successive values of q(i) i 3 1 5 6 3 1 2 3 4 1 1 2 Q: give a possible scenario after link 45 solution breaks 1 -> 2 2 -> 5 2 -> 3 5 -> 4 2 -> 4

1 -> 4 4 -> 5 5 -> 2 5 -> 3 link breaks 1 2 3 4 5 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 4

5 4 2 2 2 2 4 4 4 4 4 3 3 3 19 Naive Distributed Bellman-Ford The previous distributed version requires a node to remember all previously received estimates q(j) for all neighbours, even if they are not the best ones In practice this is a problem if we need to compute the shortest paths to not just one destination, but to a large number. A naive distributed Bellman-Ford would be as v1 except we replace eq(1) by: Distributed DistributedBellman-Ford Bellman-FordAlgorithm Algorithmv1a, v1a,BFD1a BFD1a when whennode nodei ireceives receivesnew newvalue valueq(j) q(j)from fromnode nodej jdo do eq eq(1a) (1a) q(i) q(i):= :=min min{{A(i,j) A(i,j)+ +q(j), q(j),q(i) q(i)}}

Q. does this work ? why or why not ? solution 20 Distributed Bellman-Ford, contd There is an alternative algorithm, that requires only to remember the best neighbour (pred(i)) Distributed DistributedBellman-Ford Bellman-FordAlgorithm, Algorithm,version version22BFD2 BFD2 every everynode, node,say sayi,i, maintains maintainsan anestimate estimateq(i) q(i)of ofthe thedistance distance p(i) p(i)to tosome somefixed fixednode node1; 1;initial initialconditions conditionsare arearbitrary arbitrarybut but q(1)=0 q(1)=0at atall allsteps steps from fromtime timeto totime, time,i isends sendsits itsvalue valueq(i) q(i)to toall

allits itsneighbours neighbours when whennode nodei ireceives receivesaavalue valueq(j q(j00))from fromany anyneighbour neighbourj0j0,,ititsets sets q(j q(j0))to tothe thereceived receivedvalue valueand andupdates updatesq(i) q(i)by byrecomputing recomputing 0 eq eq(2) (2) ififj0j0== ==pred(i) pred(i) then thenq(i) q(i):= :=A(i,j A(i,j00)+q(j )+q(j00)) else else q(i) q(i):= :=min min{{A(i,j A(i,j00))+ +q(j q(j00),),q(i) q(i)}} ififeq eq(2) (2)causes causesq(i) q(i)to tobe bemodified,

modified,pred(i) pred(i)isisset setto toj 0j 0 21 Distributed Bellman-Ford v2 Theorem: If the time to reliably send a message to all neighbours and perform local computations is bounded by T, then the algorithm BFD2 converges to the correct values in at most m (T+T) time units, where m is the number of steps of convergence of the centralized algorithm with same initial conditions Comment: The main difference with version 1 is that eq(2) replaces eq(1). Assume we use v2, and we start from a condition such that q(i) is indeed equal to the minimum given by eq (1) (which is what, intuitively, is true most of the time). When j is not equal to pred(i), both eq(1) and eq(2) have the same effect: the new value of q(i) is the same in both cases. In contrast, if j == pred(i), then eq (2) sets q(i) to the new value A(i,j)+q(j), whereas eq(1) sets it to minj neighbour (A(i,j)+q(j)). Eq(2) provides an upper bound on eq(1), in this case. It turns out that the algorithm still works, by the same mechanism that makes the algorithm work even when the initial conditions are arbitrary. Indeed, node i will send its new value to all remaining neighbours, who will in turn do an update and eventually, node i will receive values of q(j) that will correct the problem. In other words, if the new value of q(i) is too high (compared to what would be obtained with eq (1)), this is repaired in one round of exchanges with the neighbours. 22 Distributed Bellman-Ford v2 A possible run of algorithm v1: i 3 1 5 6 3 1 2 3 4 1 1 2 1 -> 2 2 -> 5 2 -> 3 5 -> 4 2 -> 4 1 -> 4

4 -> 5 5 -> 2 5 -> 3 link breaks 1 2 3 4 5 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 4 5

4 2 2 2 2 4 4 4 4 4 3 3 3 Q: give a possible scenario after link 45 solution breaks 23 How it is used in practice Node i computes shortest path and next hop for all network prefixes n that it heard of. Initially: D(i,n) = 0 if i directly connected to n and D(i,n) = + for any n that was never heard of. Node i receives from neighbour k latest values of D(k,n) for all n (this is the distance vector). Node i computes the best estimates according to algorithm BFD2 This converges if network is stable hello mechanism to reset computation after changes if neighbour k is no longer present, node i will no longer receive hello messages, and after a timeout, this has the same effect as if node i would receive the message from k: D(k,n)=1 for all n. Then algorithm BFD2 is run 1 D(1,n) c(i,1) i c(i,k) c(i,m) k m D(k,n) n

D(m,n) 24 Example 1 A B net dist nxt n1 0 n1,A n4 0 n4,A net dist nxt n1 0 n1,B n2 0 n2,B A n1 n4 D D net n3 n4 m3 dist nxt 0 n3,D 0 n4,D 0 m3,D m3 B n2 n3

m2 C m1 C net n2 n3 m1 m2 dist 0 0 0 0 nxt n2,C n3,C m1,C m2,C 25 Example 1 A B from A n1 0 n4 0 net dist nxt n1 0 n1,A n4 0 n4,A A D D net n3 n4 m3

dist nxt 0 n3,D 0 n4,D 0 m3,D m3 dist 0 0 1 nxt n1,B n2,B n1,A dist 0 0 0 0 1 1 nxt n2,C n3,C m1,C m2,C n3,D n3,D B n1 n4 net n1 n2 n4 n2 m2 n3 C m1

from n3 n4 m3 D 0 0 0 C net n2 n3 m1 m2 n4 m3 26 Example 1 net dist nxt B n1 n2 n3 n4 m1 m2 m3 A net dist nxt n1 0 n1,A n4 0 n4,A A n1 n4 D D net n3 n4 m3

dist nxt 0 n3,D 0 n4,D 0 m3,D m3 B n2 n3 m2 C m1 C net n2 n3 m1 m2 n4 m3 0 0 1 1 1 1 2 n1,B n2,B n2,C n1,A n2,C n2,C n2,C from n2 n3 m1 m2 n4 m3 dist 0

0 0 0 1 1 C 0 0 0 0 1 1 nxt n2,C n3,C m1,C m2,C n3,D n3,D 27 Example 1 - Final net dist nxt net dist nxt A n1 n2 n3 n4 m1 m2 m3 0 1 1 0 2 2 1 B n1 n2 n3 n4 m1 m2 m3

n1,A n1,B n4,D n4,A n4,D n4,D n4,D A D net dist nxt n1 n2 n3 n4 m1 m2 m3 1 1 0 0 1 1 0 n4,A n3,C n3,D n4,D n3,C n3,C m3,D D m3 n1,B n2,B n2,C n1,A n2,C n2,C n2,C B n1 n4

0 0 1 1 1 1 2 n2 n3 m2 C net dist nxt m1 C n1 n2 n3 m1 m2 n4 m3 1 0 0 0 0 1 1 n2,B n2,C n3,C m1,C m2,C n3,D n3,D 28 Example 1 - Failure net dist nxt B We show only the router in the next hop field A D

net dist nxt n1 n2 n3 n4 m1 m2 m3 1 1 0 0 1 1 0 A C D D C C D D m3 0 0 1 1 1 1 2 B B C A C C C B n1 n4 n1 n2 n3 n4

m1 m2 m3 n2 n3 m2 C net dist nxt m1 C n1 n2 n3 m1 m2 n4 m3 1 0 0 0 0 1 1 B C C C C D D 29 Example 1 - Failure net dist nxt B A D timeout net dist nxt n1 n2 n3

n4 m1 m2 m3 1 1 0 0 1 1 0 A C D D C C D D m3 0 0 1 1 1 1 2 B B C A C C C timeout B n1 n4 n1 n2 n3 n4 m1 m2

m3 n2 n3 m2 C net dist nxt m1 C n1 n2 n3 m1 m2 n4 m3 1 0 0 0 0 1 1 B C C C C D D 30 Example 1 - Failure net dist nxt B A D net dist nxt n1 n2 n3 n4 m1 m2

m3 2 1 0 0 1 1 0 C C D D C C D D 0 0 1 B B C m1 m2 m3 1 1 2 C C C B n1 n4 n1 n2 n3 n2 n3 m3

From C: n1 1 B n2 0 C n3 0 C m1 0 C m2 0 C n4 1 D m3 1 D m2 C net dist nxt m1 C n1 n2 n3 m1 m2 n4 m3 1 0 0 0 0 1 1 B C C C C D

D 31 Example 1 - After Failure net dist nxt B A D net dist nxt n1 n2 n3 n4 m1 m2 m3 2 1 0 0 1 1 0 C C D D C C D D m3 0 0 1 2 1 1 2 B B C C C C

C B n1 n4 n1 n2 n3 n4 m1 m2 m3 n2 n3 m2 C net dist nxt m1 C n1 n2 n3 m1 m2 n4 m3 1 0 0 0 0 1 1 B C C C C D D 32 Example 1: conclusions Example 1 illustrates

how Bellman Ford is mapped to the network concepts how topology changes are taken into account most recent announcement replaces previous ones non refreshed announcements become obsolete how distance vector carries reachability information 33 Example 2 To simplify, we identify destination with router Assume algorithm has convergedB A dest link cost A B D C E local l1 l3 l1 l1 dest link cost 0 1 1 2 2 A cost =1 l1 cost =1 l3 D D dest link cost D A B C E local l3

l3 l3 l6 0 1 2 3 1 B A C E D B cost =1 0 1 1 1 2 C dest link cost cost =1 l2 l4 l6 local l1 l2 l4 l1 l5 E E C A B D E C

cost =5 local l2 l2 l2 l2 0 2 1 3 2 dest link cost E A B D C local l4 l4 l6 l4 0 2 1 1 2 34 Example 2 we now show only table entries: to C link 2 fails B updates its table C l1 2 A C l3 l4 l6

D C B l1 l3 3 l2 C l5 C local 0 E C l4 2 35 Example 2: Link failure Just before B updates its table, A broadcasts its table with cost 2 to C B updates C l1 2 C l1 3 from A: C l1 2

A l3 l3 l4 l6 D C B l1 3 C l5 C local 0 E C l4 2 36 Example 2: Link failure B sends update to A and E A and E update C l1 4 C l1 3

from B: C l1 3 A B l1 l3 l4 l6 D C l5 C local 0 E from B: C l1 3 C l3 3 C l4 4 37 Example 2: Link failure C sends update it is ignored by E because it it less good C l1 4 A C

D l3 l4 l6 3 3 B l1 l3 C l1 C l5 E C C local 0 from C: C local 0 l4 4 38 Example 2: Link failure A broadcasts its table with cost 4 to C B updates we have a loop between A and C cost is increase by 2 at every iteration C l1 4 C

l1 5 from A: C l1 4 A l3 l3 l4 l6 D C B l1 3 C l5 C local 0 E C l4 4 39 Example 2: Link failure E now accepts announcement from C C l1 6 A

C l3 l4 l6 D 7 7 B l1 l3 C l1 l5 E C C C local 0 from C: C local 0 l5 5 40 Example 2: Link failure E sends announcements to D and B B and D send announcements to A the algorithm has converged stable state C l1 7

C l4 from B: C l4 6 A l4 l6 D from E: C l5 5 B l1 l3 6 C l5 C local 0 E from E: C l5 5 C l6 6 C l5 5 41 Conclusions from Example 2 the algorithm converges after modification of the topology, but the convergence may be very slow bounce effect

Q: during convergence time, how are routing tables ? solution 42 Assume now all link costs are equal to 1 Links l1 and l6 fail D Bdetects failure and sets costs to 1 Example 3 A dest link cost A B D C E local l3 l3 l3 l3 dest link cost 0 3 1 3 2 B A C E D A local l4 l2 l4 l4 0 3 1 1 2

C dest link cost B l2 l3 D D dest link cost D A B C E local l3 l3 l6 l6 0 1 l4 l5 E C A B D E C E local l5 l2 l5 l5 0 3

1 2 1 dest link cost E A B D C local l6 l4 l6 l5 0 2 1 1 1 43 Example 3 A A dest link cost A B D C E local l3 l3 l3 l3 from dest A B,C D E D dest link cost 0

3 1 3 2 A B D C E A: cost 0 3 1 2 A l3 D dest link cost D A B C E local l3 l3 l3 l3 A 0 1 4 4 3 local l3 l3 l3 l3 from dest A B,C

D E D dest link cost 0 5 1 5 4 A B D C E B: cost 1 4 0 3 A l3 D dest link cost D A B C E local l3 l3 l3 l3 0 1 4 4 3 local l3 l3 l3 l3

from dest A B,C D E D 0 3 1 3 2 A: cost 0 5 1 3 A l3 D dest link cost D A B C E local l3 l3 l3 l3 0 1 6 6 5 44 Conclusion from Example 3 The costs to C, B, E grow unbounded Count to Infinity the true costs are infinite Convergence to a stable state if we set = large number e.g. RIP: = 16

Split Horizon a heuristic to prevent this if A routes packets to X via B, it does not announce this route to B 45 Example 3: with Split Horizon A B dest link cost A B D C E local l3 l3 l3 l3 dest link cost 0 3 1 3 2 B A C E D A local l4 l2 l4 l4 0 3 1 1 2

C dest link cost B l2 l3 D D dest link cost D A B C E local l3 l3 l6 l6 0 1 l4 l5 E C A B D E C E local l5 l2 l5 l5 0 3

1 2 1 dest link cost E A B D C local l6 l4 l6 l5 0 2 1 1 1 46 Example 3: with Split Horizon A dest link cost A B D C E local l3 l3 l3 l3 from A: dest cost A 0 0 3 1 3 2 A l3 D D

dest link cost D A B C E local l3 l3 l6 l6 0 1 47 Split horizon A Split horizon cuts the process of counting to infinity dest link cost A B D C E local l3 l3 l3 l3 from D: dest cost D 0 B,C,E 0 1 A l3 D

D dest link cost D A B C E local l3 l3 l6 l6 0 1 48 Split horizon may fail B from dest A B C D E: cost 1 1 dest link cost B A C E D local l4 l2 l4 l4 0

1 1 dest link cost B l2 l4 l5 E C C A B D E C E local l5 l2 l5 l5 0 3 1 2 1 dest link cost E A B D C local l6 l4 l6 l5 0 1

1 49 Split horizon may fail B from dest A D E dest link cost B A C E D local l2 l2 l4 l2 0 4 1 1 3 C dest link cost B l2 l4 l5 E C: cost 3 2 1 C A B D E

C E dest link cost E A B D C local l6 l4 l6 l5 0 1 1 local l5 l2 l5 l5 0 3 1 2 1 from C: dest cost B 1 50 Split horizon may fail B dest link cost B A C E D local l2

l2 l4 l2 0 4 1 1 3 C dest link cost B l2 l4 l5 E from dest A B C D B: cost 4 0 1 3 C A B D E C E local l5 l2 l5 l5 0 3 1

2 1 dest link cost E A B D C local l4 l4 l4 l5 0 5 1 4 1 51 Conclusion: Distance Vector convergence to stable state may be slow after changes count to infinity must be prevented by setting a maximum distance 52 3. Distance Vector Protocols RIP Distance vector protocol Metric - hops Network span limited to 15 = 16 Split horizon Destination network identified by IP address Netmasks in RIPv2 Encapsulated as UDP packets, port 520 Largely implemented (routed on Unix) Broadcast every 30 seconds or when update detected Route not announced during 3 minutes cost becomes Authentication in RIPv2 by MD5 (shared secret) 53

IGRP (Interior Gateway Routing Protocol) Proprietary protocol by CISCO Metric that estimates the global delay Maintains several routes of similar cost load sharing Takes into account netmasks No limit of 15 number of routers included in messages Broadcast every 90 sec 54 Metric example Metric Trans = 10000000/Bandwidth (time to send 10 Kb) delay = (sum of Delay)/10 m = [K1*Trans + (K2*Trans )/(256-load) + K3*delay] default: K1=1, K2=0, K3=1, K4=0, K5=0 if K5 0, m = m * [K5/(Reliability + K4)] Bandwidth in Kb/s, Delay in s At Venus: Route for 172.17/16: Metric = 10000000/784 + (20000+1000)/10 = 14855 At Saturn: Route for 12./8: Metric = 10000000/224 + (20000 + 1000)/10 = 46742 55 3. Load Dependent Routing We come back in this section to what routing protocols do. Instead of maximizing a path quality metric (nb hops, delay) assume we want to maximize the total network utility for example: total transported flows see congestion control chapter for other definitions how should routing be done ? Q1: show an example where shortest path routing does not provide the optimal total flow (where path cost is static) solution One solution might be to take delay as the path cost high load on a link => high cost => link is less used however, this does not solve the problem: there is the Braess paradox 56 Braess Paradox (1) Assume all flows pick the route with shortest delay Assume parallel paths exist and flows can make use of them Delay is function of load as given below; link 5 is (temporarily) closed Total offered load is b0 = 6 Gb/s For example, if we split traffic into : route 1-3: b = 1, route 2-4 b = 5

the delay along route 1-3 is 61, along route 2-4 is 105 thus the link costs will change and routing decisions will change also Eventually, there will be an equilibrium (called Wardrop Equilibrium) delay is equal on all competing routes Q: compute the equilibrium traffic flow on every link Solution 57 Braess Paradox (2) Q: same question when we open link 5 with delay function: Solution 58 Braess Paradox With shortest delay routing, adding a new link may decrease overall throughput Thus shortest delay routing is not either a global optimum 59 Optimal Routing One can change the objective of routing: instead of computing shortest paths,one could solve a global optimization problem: minimize total delay subject to flow constraints this is a well posed optimization problem the optimal solution depends on all flows but it can be implemented in a distributed algorithm similar to TCP congestion control ; see [BertsekasGallager92] Q. Can you imagine a way to use classical routing (like distance vector, which finds shortest paths) and still find the optimum network utility ? solution 60 Conclusion Distance vector is smart Fully distributed, little information stored Largely deployed (Unix BSD routed) Simplicity But: slow convergence Not suited for large and complex networks Link State protocols should be used instead 61

Review Questions Explain the following terms: distance vector bounce effect count to infinity split horizon Bellman Ford RIP, IGMP source routing Explain why shortest path routing is not necessarily a globally optimum What is the Braess paradox ? 62 Solutions 63 1. Introduction Why were routing protocols invented Connectionless Network Layer assumes routing tables are maintained at hosts and routers used by Packet Forwarding Routing = control method maintain routing tables automatically in routers At host normally done by default rules plus ICMP redirect in old times: was done also by a routing protocol (RIP) Compare to: LANs connected by bridges operate at layer 2 like connectionless packet forwarders Q. How do they maintain routing information ? A. By learning from the packets they observe; broadcast is used to bootstrap back 64 Source Routing A B 2.2.4 SA DA RI data A B 2.2.4 1 2

A 1 IS 3 IS 4 2 1 IS 2 3 3 A B 2.2.4 1 2 IS 4 B 3 Q. What are the routes that can be used from A to B ? A. A224 A234 A2434 A334 A3224 A3234 back A route is described by a sequence of port numbers 65 Example Apply the theorem: write pk(i,1), pred(i) and draw the shortest paths to node 1. 3 1 5 6 3 1

2 3 4 1 1 1 k\i 0 1 2 3 i 1 pred(i) 1 1 0 0 0 0 2 1 1 1 2 1 3 7 3 4 5 1 1 2 1 2 3 5 4 1 5 4

back 66 Impact of Initial Conditions Example: Q. does the algorithm converge to the shortest path with initial condition as shown ? A. yes 3 1 5 6 3 1 2 3 4 1 1 1 k\i 0 1 2 3 4 1 0 0 0 0 0 2 0 1 1 1 1 3 0 1 2 3 3 4

0 1 1 1 1 5 0 1 2 2 2 k\i 0 1 2 1 0 0 0 2 6 1 1 3 1 1 3 4 1 1 1 5 0 2 2 back 67 Distributed Bellman-Ford v1 A possible run of algorithm v1: i 3 1 5

6 3 1 2 3 4 1 1 2 Q: give a possible scenario after link 45 back breaks 1 -> 2 2 -> 5 2 -> 3 5 -> 4 2 -> 4 1 -> 4 4 -> 5 5 -> 2 5 -> 3 link breaks 5 -> 3 1 2 3 4 5 0 0 0 0 0 0 0 0 0 0

1 1 4 1 7 4 1 7 5 4 1 7 4 4 1 7 2 4 1 7 2 3 1 7 2 3 1 4 2 3 4 does as if received from 5 5 does as if received from 4 and continue computations from there 0 1 4 2 4 0 1 5

2 4 68 Naive Distributed Bellman-Ford The previous distributed version requires a node to remember all previously received estimates q(j) for all neighbours, even if they are not the best ones In practice this is a problem if we need to compute the shortest paths to not just one destination, but to a large number. A naive distributed Bellman-Ford would be as v1 except we replace eq(1) by: Distributed DistributedBellman-Ford Bellman-FordAlgorithm Algorithmv1a, v1a,BFD1a BFD1a when whennode nodei ireceives receivesnew newvalue valueq(j) q(j)from fromnode nodej jdo do eq eq(1a) (1a) q(i) q(i):= :=min min{{A(i,j) A(i,j)+ +q(j), q(j),q(i) q(i)}} Q. does this work ? why or why not ? A. no. q(i) can only decrease. So if we start from initial conditions as in example Impact of Initial Conditions , the algorithm will not converge to the right value. It gets stuck with a low value. It is possible to show that it works if all initial conditions are above the final values, for example q(j)=1 initially. But even then, it will not work if there is a topology change, since this is equivalent to starting from different initial conditions back 69 Distributed Bellman-Ford v2 A possible run of algorithm v1: i

3 1 5 6 3 1 2 3 4 1 1 2 Q: give a possible scenario after link 45 back breaks 1 1 -> 2 2 -> 5 2 -> 3 5 -> 4 2 -> 4 1 -> 4 4 -> 5 5 -> 2 5 -> 3 link breaks 5 2 2 5 -> -> -> -> 3 3 5 3 0 0

0 0 0 0 0 0 0 0 5 0 5 4 0 0 0 0 0 2 3 4 5 1 1 4 1 7 4 1 7 5 4 1 7 4 4 1 7 2

4 1 7 2 3 1 7 2 3 1 4 2 3 4 does as if received from 5 pred(4) 1 4 2 3 does as if received from 4 == pred(5) 1 4 2 1 2 1 7 2 1 7 2 4 1 5 2 4 70 Conclusions from Example 2 Q: during convergence time, how are routing tables ? A: they are incorrect there are loops packets are discarded (TTL expires) back 71

3. Load Dependent Routing Q. show an example where shortest path routing does not provide the optimal total flow (where path cost is static) A. assume all data flow goes from B to E: Static shortest path routing will pick the direct link BE only instead of distributing the load also on some of the longer links (BADE and BCE) A cost =1 l1 cost =1 l3 D B cost =1 l2 l4 l6 cost =1 l5 E E C cost =5 back 72 Braess Paradox (1) A. there are two paths 1: links 1, 3; 2: links 2,4 let bi be the traffic on path I Delay equations: 50+ 11b1 = 50 + 11b2 Total flow b1 + b2 = b0 equilibrium is for b1 = b2 = 3 delay is 83 back 73 Braess Paradox (2)

Q: same question when we open link 5 with delay function: A: there are three paths 1: links 1, 3; 2: links 2,4; 3: links 1, 5, 4 delay equations 50 + 11b1 + 10b3 = 50 + 11b2 + 10b3 = 10 + 10b1 + 10 b2 + 21 b3 total flow b1 + b2 + b3 = b0 We find b1= b2 = b3 = 2 Gb/s The total delay on all paths is the same, equal to 92 : larger than before! back 74 Optimal Routing One can change the objective of routing: instead of computing shortest paths,one could solve a global optimization problem: minimize total delay subject to flow constraints this is a well posed optimization problem the optimal solution depends on all flows but it can be implemented in a distributed algorithm similar to TCP congestion control ; see [BertsekasGallager92] Q. Can you imagine a way to use classical routing (like distance vector, which finds shortest paths) and still find the optimum network utility ? A. Let a centralized network management procedure update the link costs (used by distance vector routing). given link costs ci and traffic matrix compute total throughput or average delay ( a hard optimization problem, solved with heuristics) every few minutes, update the link costs in all routers let the routing algorithm compute new paths back 75

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