1. Wide Area Networks (WANs), Routing, and Shortest Paths
Part 3.1
Wide Area Networks (WANs),
Routing, and Shortest Paths
Robert L. Probert, SITE, University of Ottawa
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2. Motivation Connect multiple computers Span large geographic distance
Cross public right-of-way
Streets
Buildings
Railroads
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3. Building Blocks Point-to-point long-distance connections
Packet switches
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4. Packet Switch Hardware device Connects to Forwards packets
Other packet switches
Computers
Forwards packets
Uses addresses
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5. Illustration of a Packet Switch
Special-purpose computer system
CPU
Memory
I/O interfaces
Firmware
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6. 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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7. Illustration of a WAN Interconnections depend on Estimated traffic
Reliability needed
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8. Store and Forward Basic paradigm used in packet switched network
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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9. Addressing in a WAN Need Two-part address
Unique address for each computer
Efficient forwarding
Two-part address
Packet switch number
Computer on that switch
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10. 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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11. Next-Hop Forwarding Performed by packet switch Uses table of routes
Table gives next hop
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12. Forwarding Table Abbreviations
Many entries point to same next hop
Can be condensed (default)
Improves lookup efficiency
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13. 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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14. Relationship of Routing To Graph Theory
Node models switch
Edge models connection
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15. 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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16. 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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17. 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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18. 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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19. Distance Vector Algorithm
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20. 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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21. 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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22. Question: What are the problems with this method?
Proposal
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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23. Distance Vector Routing
Global view
Info at node
Distance to node A B C D E F G 1
A’s view: vector
Dest
Cost
NextHop B 1 C D - E F G
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24. Distance Vector Routing
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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25. Distance Vector Routing
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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26. The Count-to-infinity Problem
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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27. 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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28. Link State Routing
Assumptions: 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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29. Link State Routing Mechanisms
Reliable dissemination of link-state information a flooding.
Calculation of routes from the sum of all the accumulated link-state knowledge.
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30. Reliable Flooding
Question: 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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31. Reliable Flooding
Question: 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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32. 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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33. Dealing with LSPs An 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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34. 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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35. 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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36. 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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37. Dijkstra’s Algorithm
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38. 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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39. 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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40. 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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41. 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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42. Recent WAN Technologies
SMDS
Offered by phone companies
Not as popular as Frame Relay
Frame Relay
Widely used commercial service
ATM
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43. 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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44. Summary Wide Area Networks (WANs) WAN addressing 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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45. Summary (continued) Routing Routing tables created
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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46. Summary (continued) Example WAN technologies ARPANET X.25 SMDS
Frame Relay
ATM
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