1. ECE 4450:427/527 - Computer Networks Spring 2017
Dr. Nghi Tran
Department of Electrical & Computer Engineering
Lecture 6.3: Routing
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

2. Internetworking: Discussions
For Internetworking, we shall look at few sub-problems:
Interconnect links of the same type: Switches
We consider an important of class switch: Bridges to interconnect Ethernet segments.
We also look a way to interconnect disparate networks and links: Gateways, or now mostly known as routers. We shall focus on the IP
Once we are able to interconnect a whole lot of links and networks with switches and routers, we will look at a way to find a suitable path, or route through a network:
Paths that are efficient, loop free, etc.: Routing
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

3. Recall: IP Routing Table
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

4. What is Routing?
Construct directions from starting point to destination
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

5. Forwarding vs Routing Forwarding: data plane Routing: control plane
Directing a data packet to an outgoing link
Individual router using a forwarding/routing table
Routing: control plane
Computing paths the packets will follow
Routers talking amongst themselves
Individual router creating a forwarding table
Routing can be simply understood as a process by which routing table is built 5
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

6. Why Does Routing Matter?
End-to-end performance
Quality of the path affects user performance
Propagation delay, throughput, and packet loss
Use of network resources
Balance of the traffic over the routers and links
Avoiding congestion by directing traffic to lightly-loaded links
Transient disruptions during changes
Failures, maintenance, and load balancing
Limiting packet loss and delay during changes
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

7. Different Types of Routing
Routing in a GPS device
Routing in computer networks
Shortest path
Smallest delay
Highest reliability
Avoid congested nodes
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

8. Routing Network as a Graph
The basic problem of routing is to find the lowest-cost path between any two nodes
The cost of a path equals the sum of the costs of all the edges that make up the path
Cost: delay, financial cost, probability of failure, etc.
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

9. Routing
For a simple network, we can calculate all shortest paths and load them into some nonvolatile storage on each node.
Such a static approach has several shortcomings
It does not deal with node or link failures
It does not consider the addition of new nodes or links
It implies that edge costs cannot change
What is the solution?
Need a distributed and dynamic protocol
Two main classes of protocols
Distance Vector
Link State
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

10. Distance Vector: Algorithm
Construct a one-dimensional array (vector) of distances to all other nodes, with assumption that each node knows the cost of the link to each of its directly connected neighbors
Exchange info with immediate neighbors
Update distances based on received information
Stop sending updates as soon as no change occurs
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

11. Distance Vector
Initial distances stored at each node (global view) (assume unit cost for each link)
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

12. Distance Vector
Dest
Cost
Next Hop B 1 C D  – E F G
Initial routing table at node A, assume unit cost for each link
Now, how about node B?
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

13. Distance Vector: Local View (initial)
Dest
Cost
Next Hop B 1 C D  – E F G
Dest
Cost
Next Hop A 1 C D  – E F G
Dest
Cost
Next Hop A 1 B D E  – F G
Dest
Cost
Next Hop A  – B C 1 E F G
Dest
Cost
Next Hop A 1 B  – C D F G
Dest
Cost
Next Hop A 1 B  – C D E G
Dest
Cost
Next Hop A  – B C D 1 E F
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

14. Distance Vector – 1st Update for A
1st Update: Every node sends to its directly connected neighbors its personal list of distances
Dest
Cost
Next Hop B 1 C D  – E F G
1st Update for A
Dest
Cost
Next Hop B 1 C D 2 E F G
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

15. Distance Vector – 1st Update for B?
Dest
Cost
Next Hop A 1 C D 2 E F G  –
Dest
Cost
Next Hop A 1 C D  – E F G
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

16. Distance Vector – 1st Update
Dest
Cost
Next Hop B 1 C D 2 E F G
Dest
Cost
Next Hop A 1 C D 2 E F G  –
Dest
Cost
Next Hop A 1 B D E 2 F G
Dest
Cost
Next Hop A 2 C B 1 E  – F G
Dest
Cost
Next Hop A 1 B 2 C D  – F G
Dest
Cost
Next Hop A 1 B 2 C D G E
Dest
Cost
Next Hop A 2 F B  – C D 1 E
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

17. Distance Vector – 2nd Update
Dest
Cost
Next Hop B 1 C D 2 E F G
Dest
Cost
Next Hop A 1 C D 2 E F G 3
Dest
Cost
Next Hop A 1 B D E 2 F G
Dest
Cost
Next Hop A 2 C B 1 E 3 F G
Dest
Cost
Next Hop A 1 B 2 C D 3 F G
Dest
Cost
Next Hop A 1 B 2 C D G E
Dest
Cost
Next Hop A 2 F B 3 D C 1 E
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

18. Distance Vector: Global View
Final distances stored at each node (global view)
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

19. Distance Vector
The other common name for this class of algorithm is Bellman-Ford, after its inventors.
As we can see, it usually takes a number of exchanges/updates between neighbors before each node has complete routing table.
The process of getting consistent routing information to all nodes is called convergence: vary
We have two different circumstances under which a node decides to send a routing update
Periodic update
Triggered update
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

20. Failure of Link F-G When a node detects a link failure
F detects that link to G has failed (how?)
F sets distance to G to infinity and sends update to A
A sets distance to G to infinity since it uses F to reach G
A receives periodic update from C with 2-hop path to G
A sets distance to G to 3 and sends update to F
F decides it can reach G in 4 hops via A
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

21. Failure of Link F-G: Updated Tables
A/Dest
Cost
Next Hop B 1 C D 2 E F G 3
B/Dest
Cost
Next Hop A 1 C D 2 E F G 3
C/Dest
Cost
Next Hop A 1 B D E 2 F G
D/Dest
Cost
Next Hop A 2 C B 1 E 3 F G
E/Dest
Cost
Next Hop A 1 B 2 C D 3 F G 4
F/Dest
Cost
Next Hop A 1 B 2 C D 4 E G
G/Dest
Cost
Next Hop A 3 D B C 2 1 E F 4
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

22. Drawback Suppose the link from A to E goes down
In the next round of updates, A advertises a distance of infinity to E, but C advertise a distance of 2 to E
Depending on the exact timing of events, the following might happen
Node B, upon hearing that E can be reached in 2 hops from C, concludes that it can reach E in 3 hops and advertises this to A
Node A concludes it can reach E in 4 hops and advertises this to C
Node C concludes that it can reach E in 5 hops; and so on.
This cycle stops only when the distances reach some number that is large enough to be considered infinite
Count-to-infinity problem
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

23. Solutions
One technique to improve the time to stabilize routing is called split horizon
When a node sends a routing update to its neighbors, it does not send those routes it learned from each neighbor back to that neighbor
For example, if B has the route (E, 2, A) in its table, then it knows it must have learned this route from A, and so whenever B sends a routing update to A, it does not include the route (E, 2) in that update
In a stronger version of split horizon, called split horizon with poison reverse
B actually sends that back route to A, but it puts negative information in the route to ensure that A will not eventually use B to get to E
For example, B sends the route (E, ∞) to A
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

24. Routing Information Protocol (RIP)
Early routing protocol for IP networks
Distance-vector algorithm where vertices are networks and not hosts
Valid hop count (distances) 1-15, with 16 representing infinity
Limited to fairly small networks
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

25. Metric/Cost in Real World
Can we just assign a cost of 1 to all links?
Certainly, there are so many ways to define cost:
Number of packets queued waiting to be transmitted
Consider both bandwidth and latency
In the current real world
Common approach: a constant/link bandwidth
It means metric changes rarely if at all and only under the control of network administrator
Why?
i) Dynamically changing metrics are too unstable;
ii) Many networks today lack the great disparity of speeds and latencies
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

26. Distance Vector vs. Link-State
each node talks only to its directly connected neighbors …
but it tell them everything it has learned, i.e., distance to all nodes
Link-state:
each node talks to all other nodes…
but it tells them only what it knows for sure, i.e., state of its directly connected links
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

27. Link-State Routing
Strategy: Send to all nodes (not just neighbors) information about directly connected links (not entire routing table) and associated cost.
Rely on two key mechanisms:
Reliable flooding: Make sure all nodes get the above information of other nodes
Route calculation: Once a node has a copy of the information from every other node, it is able to compute a complete map of the network, and then can decide the best route to each destination
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

28. Reliable Flooding
Objective: Make sure all nodes get the link-state information of other nodes: Knowledge of directly connected neighbors and associated cost for each node
Each node creates an update packet called link-state packet (LSP) with the following information:
ID of the node that created LSP
Cost of link to each directly connected neighbor
Sequence number (SEQNO)
Time-to-live for this packet
Reliable flooding: Make sure all nodes get LSP from the other nodes.
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

29. Reliable Flooding
Transmission of LSPs between adjacent routers using ACK and transmission. But how to send an LSP to all nodes?
We need some more steps to reliably flood an LSP to all nodes: controlled flooding
A node x receives a copy of LSP originated from y.
x then needs to check if it has already had a copy. If not, store it. If it has, compare sequence number, keep a newer one.
If the received LSP is new, x sends a copy of LSP to all neighbors, except the one from which LSP was received (why?).
Neighbors then turn around and do the same thing: most recent copy of LSP reaches all nodes.
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

30. Reliable Flooding Reliable Flooding Flooding of link-state packets.
(a) LSP arrives at node X;
(b) X floods LSP to A and C;
(c) A and C flood LSP to B (but not X);
(d) flooding is complete
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

31. Route Calculation
Now, assume a given node has copies of LSPs from every other node:
it is able to compute a complete map for the topology of the network
and from this map it is able to decide the best routes to each destination
But how to decide/calculate the best route to each destination?
The solution is based on a well-known algorithm from graph theory – Dijkstra’s shortest-path algorithm
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

32. Shortest-Path Problem: Statement
Given: network topology with link costs
c(x,y): link cost from node x to node y
Infinity if x and y are not direct neighbors
Compute: least-cost paths to all nodes
From a given source u to all other nodes
For each node a: u stores a’s predecessor node along path from u to a, & cost. 2 1 3 1 4 u a 2 1 5 4 3
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

33. Dijkstra’s Algorithm: Overview
Iterative algorithm for a given source node u:
After k iterations, know least-cost path to k nodes
S: set of nodes whose least-cost path known
Initially, S = {u} where u is the source node
Add one node to S in each iteration
D(a): current cost of path from source to node a
Initially, D(a) = c(u, a) for all nodes a adjacent to u
… and D(a) = ∞ for all other nodes a
Continuously update D(a) as shorter paths are learned
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

34. Dijkstra’s Algorithm: Implementation
1 Initialization:
2 S = {u}
3 for all nodes {a}
If a adjacent to u {
D(a) = c(u,a)
else D(v) = ∞ 7
8 Loop
9 Find b not in S with the smallest D(b)
10 Add b to S, store D(b) and P(b) (predecessor node)
11 update D(a) for all {a} adjacent to b and not in S:
D(a) = min{D(a), D(b) + c(b,a)}
13 until all nodes in S
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

35. Example 3 2 1 4 5 3 2 1 4 5 3 2 1 4 5 3 2 1 4 5
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

36. Example (Cont.) 3 2 1 4 5 3 2 1 4 5 3 2 1 4 5 3 2 1 4 5
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

37. Dijkstra’s Table Step S v w x y z s t . 7 u,w,v,x,y,z,s,t 3 u 2 5 6 8
D(v)
P(v)
D(w)
P(w)
D(x)
P(x)
D(y)
P(y)
D(z)
P(z)
D(s)
P(s)
D(t)
P(t) . 7
u,w,v,x,y,z,s,t 3 u 2 5 6 8
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

38. Shortest-Path Tree Shortest-path tree from u Routing table at u vw
(u,w) x y z
link s t 3 2 1 4 5 u v w x y z s t
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

39. Open Shortest First Path (OSPF)
It is the most widely used link-state routing protocols in practice
Open: Publicly available
SPF: Alternative name for link-state
OSPF adds a number of features to the basic link-state
Authentication: Make sure all nodes can be trusted
Additional hierarchy: Network domain divided further in to areas: a router might just need to get to a right area
Load balancing: Allow multiple routes to the same place to be assigned the same cost: traffic distributed evenly to those routers
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

40. Distance Vector vs. Link-State
Distance vector: each node talks only to its directly connected neighbors, but it tell them everything it has learned, i.e., distance to all nodes
Link-state: each node talks to all other nodes, but it tells them only what it knows for sure, i.e., state of its directly connected links
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

41. Distance Vector vs. Link-State
Who is the winner?
Robustness: what happens if router malfunctions, misbehaves?
LS:
node can advertise incorrect link cost
each node computes only its own table, providing a degree of robustness
DV:
DV node can advertise incorrect path cost
each node’s table used by others , error propagate thru network
Message complexity
LS: with n nodes, E links, O(nE) msgs sent . A lot of information needs to be stored.
DV: exchange between neighbors only
Speed of Convergence
LS: O(n2) algorithm requires O(nE) msgs
DV: convergence time varies
may be routing loops
count-to-infinity problem
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

42. Routing in the Internet
Routing protocols we have learned so far: idealization, all routers identical, flat network
Internet: Network of hundreds of thousands of networks: Not possible to directly using those protocols: they do not scale to those kinds of numbers!!!
We need something else!!!
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

43. Routing in the Internet
Internet is organized as autonomous systems (AS) each of which is under the control of a single administrative entity
Autonomous System (AS)
corresponds to an administrative domain
examples: University, company, backbone network, as may the network of a single ISP
AS chooses its own routing protocol, e.g., distance-vector or link-state
Divide routing problem in two parts:
Intra-domain: We have already learned
Inter-domain: Border Gateway Protocol (BGP) (BGP-v4)
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527

44. BGP Few books dedicated to BGP Key points:
Assumes the Internet is an arbitrarily interconnected set of AS‘s
Impossible to define optimal path
Advertise only reachability: complete paths as an enumerated lists of ASs to reach a particular network
Does not belong to either distance-vector or link-state
For further discussions (high points), see Chapter 4.1.2
Dr. Nghi Tran (ECE-University of Akron)
ECE 4450:427/527