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Part 3.1Wide Area Networks (WANs),

Routing, and Shortest Paths

Robert L. Probert, SITE, University of Ottawa

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Motivation Connect multiple computers Span large geographic distance Cross public right-of-way

Streets Buildings Railroads

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Building Blocks Point-to-point long-distance connections Packet switches

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Packet Switch Hardware device Connects to

Other packet switches Computers

Forwards packets Uses addresses

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Illustration of a Packet Switch

Special-purpose computer system CPU Memory I/O interfaces Firmware

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Building a WAN Place one or more packet switches at each

site Interconnect switches

LAN technology for local connections Leased digital circuits for long-distance

connections

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Illustration of a WAN

Interconnections depend on Estimated traffic Reliability needed

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Store and Forward Basic paradigm used in packet switched network Packet

Sent from source computer Travels switch-to-switch Delivered to destination

Switch “Stores” packet in memory Examines packet’s destination address “Forwards” packet toward destination

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Addressing in a WAN Need

Unique address for each computer Efficient forwarding

Two-part address Packet switch number Computer on that switch

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Illustration of WAN Addressing

Two part address encoded as integer Higher-order bits for switch number Low-order bits for computer number

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Next-Hop Forwarding

Performed by packet switch Uses table of routes Table gives next hop

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Forwarding Table Abbreviations

Many entries point to same next hop Can be condensed (default) Improves lookup efficiency

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Source of Routing Table Information Manual

Table created by hand Useful in small networks Useful if routes never change

Automatic routing Software creates/updates table Needed in large networks Changes routes when failures occur

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Relationship of Routing To Graph Theory

Graph Node models switch Edge models connection

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Shortest Path Computation Algorithms from graph theory No central authority (distributed computation) A switch

Must learn route to each destination Only communicates with directly attached

neighbors

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Illustration of Minimum Weight Path

Label on edge represents “distance” Possible distance metric

Geographic distance Economic cost Inverse of capacity

Darkened path is minimum 4 to 5

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Algorithms for Computing Shortest Paths Distance Vector (DV)

Switches exchange information in their routing tables

Link-state Switches exchange link status information

Both used in practice

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Distance Vector Periodic, two-way exchange between

neighbors During exchange, switch sends

List of pairs Each pair gives (destination, distance)

Receiver Compares each item in list to local routes Changes routes if better path exists

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Distance Vector Algorithm

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Distance Vector Intuition Let

N be neighbor that sent the routing message V be destination in a pair D be distance in a pair C be D plus the cost to reach the sender

If no local route to V or local routed has cost greater than C, install a route with next hop N and cost C

Else ignore pair

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Example of Distance Vector Routing

Consider transmission of one DV message Node 2 send to 3, 5, and 6 Node 6 installs cost 8 route to 2 Later 3 sends update to 6 6 changes route to make 3 the next hop for

destination 2

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Proposal

4

36

21

9

1

1D

A

FEB

C

Each node discovers its neighbors and tells one specific node (the leader) what they find. The leader runs an algorithm to solve the all-pairs shortest path problem, computing routing tables for each node. The leader communicates the routing table to each node, which stores it in non-volatile memory in each of the nodes.

Question: What are the problems with this method?

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Distance Vector Routing

D

G

A

F

E

B

C

Info at node

Distance to nodeA B C D E F G

A 0 1 1 1 1B 1 0 1C 1 1 0 1D 1 0 1E 1 0F 1 0 1G 1 1 0

Dest Cost NextHop

B 1 B

C 1 C

D -

E 1 E

F 1 F

G -

Global view

A’s view: vector

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Distance Vector Routing

D

G

A

F

E

B

C Every node starts by building it’s own local view of what nodes are 1 hop away.

Next, every node sends its vector to its directly connected neighbors.

F tells A that it can reach G at cost 1. A knows it can reach F at cost 1, so it updated its own vector to indicate that it can reach G at cost 2. If A were to discover another route to G at a cost higher than 2, it would ignore it and leave its vector as it is.

After a few iterations of these exchanges, the routing table converges to a consistent state.

Question: How would this method deal with link failures?

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Distance Vector Routing

D

G

A

F

E

B

C Periodic updates: Every t seconds, send your local info to your neighbors. This allows other nodes to know that you are running.

Triggered updates: Every time you learn new information from a neighbor that leads you to update your local vector, you send the recomputed vector to all your neighbors.

Question: How can you detect that a node has failed?

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The Count-to-infinity Problem

D

G

A

F

E

B

C

Link (A,E) goes down. A periodic update kicks in and A advertises that its distance to E is infinity. At about the same time, B and C tell each other that they can reach E in 2 hops. B knows now that it can’t communicate with E via A, but concludes that it can send traffic to C which says it can reach A in 2 hops. Now, B is thinking that its distance to E is 3 hops and it lets A know about that. Argh, now A is going to think it can reach E in 4 hops and it will tell C of this “fact”… Where does this end?

The routing table doesn’t converge or stabilize.

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Link-State Routing Overcomes instabilities in DV Pair of switches periodically

Test link between them Broadcast link status message

Switch Receives status message Computes new routes Uses Dijkstra’s algorithm

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Link State RoutingAssumptions: Each node can discover the state of

the link to its neighbors and the cost of each link.

Basic principle: If every node knows how to reach its directly connected neighbors, one can spread the local knowledge of each node to all other nodes in the network so that every node can construct a the network weighted graph. With this knowledge, a node can always determine the shortest path to any other node in the network.

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Link State Routing MechanismsReliable dissemination of link-state information flooding.Calculation of routes from the sum of all the accumulated link-state knowledge.

(a)

X A

C B D

(b)

X A

C B D

(c)

X A

C B D

(d)

X A

C B D

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Reliable FloodingQuestion: If you are a node and you want to tell

your neighbors the state of the links incident to you, what should be contained in the information packets that you pass to them?

Underlying Problems:A packet should reflect the state of your links at a given point in time.You need to ensure that old link-state information eventually stops circulating around the network.

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Reliable FloodingQuestion: How can one guarantee that the link-

state packets sent from a node will definitely arrive at its neighbors?

Question: How does one deal with the possibility of having multiple, contradictory copies of a node’s link-state packets circulate the network at the same time?

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Link-State Packet (LSP) CreatorID: identity of the packet creator. NeighborsList: a list of tuples like <NeighborId, link cost>. SequenceNumber: a counter value that places this packet in the context of all LSPs sent out by the creator of this packet. TTL: time to live in number of hops.

Question: When should a node send out an LSP?

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Dealing with LSPsAn LSP is sent out when:

A periodic time expires. A topology change is detected.

Important: Routing messages, that is LSPs, are overhead in the network and they should be kept to a minimum.

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Route Calculation(based on Dijkstra’s shortest-path algorithm)

Assumptions: A node constructs a graph to represent the network to the best of its

knowledge (received LSPs). N: |V|, number of nodes. l(i,j): Non-negative weight of edge (i,j). M: nodes set of nodes incorporated so far for some node s. C(n): cost of the path from node s to node n.

Algorithm for a node s:M = {s}for each n in N-{s} do: C(n)=l(s,n)while (N not equal M)

M=M U{w} such that C(w)=min{for all w in (N-M)}for each n in (N-M) do: C(n)=min{C(n), C(w)+l(w,n)}

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Example of Link-State Information

Assume nodes 2 and 3 Test link between them Broadcast information

Each node Receives information Recomputes routes as needed

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Dijkstra’s Shortest Path Algorithm Input

Graph with weighted edges Node, n

Output Set of shortest paths from n to each node Cost of each path

Called Shortest Path First (SPF) algorithm

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Dijkstra’s Algorithm

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Algorithm Intuition

Start with self as source node Move outward At each step

Find node u such that it Has not been considered Is “closest” to source

Compute Distance from u to each neighbor v If distance shorter, make path from u go through v

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Result of Dijkstra’s Algorithm

Example routes from node 6 To 3, next hop = 3, cost = 2 To 2, next hop = 3, cost = 5 To 5, next hop = 3, cost = 11 To 4, next hop = 7, cost = 8

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Properties of Link-State Routing

Stabilizes quickly. Keeps routing control traffic low. Responds rapidly to topology changes. Doesn’t scale well: the amoung of information stored in each node is large.

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Early WAN Technologies ARPANET

Historically important in packet switching Fast when invented, slow by current standards

X.25 Early commercial service Still Used More popular in Europe

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Recent WAN Technologies SMDS

Offered by phone companies Not as popular as Frame Relay

Frame Relay Widely used commercial service Offered by phone companies

ATM

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Assessment of ATM In practice, failed to deliver on promise Switches initially too expensive for LAN QoS difficult to implement cost-effectively

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Summary Wide Area Networks (WANs)

Span long distances Connect many computers Built from packet switches Use store-and-forward

WAN addressing Two-part address Switch/computer

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Summary (continued) Routing

Each switch contains routing table Table gives next-hop for destination

Routing tables created Manually Automatically

Two basic routing algorithms Distance vector Link state

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Summary (continued) Example WAN technologies

ARPANET X.25 SMDS Frame Relay ATM