1. Routing
Outline
Algorithms
Scalability
Spring 2003
CS 332

2. Give Credit:
Many of the figures I’ve used in this set of slides here are from the Prentice-Hall text “Computer Networks” (3rd Edition), by Andrew Tanenbaum.
Spring 2003
CS 332

3. Overview Forwarding vs Routing
forwarding: to select an output port based on destination address and routing table
routing: process by which routing table is built
Forwarding table optimized for next hop lookup
Routing table optimized for tracking changes in topology and calculating “best” routes.
Problem: Find lowest cost path between two nodes
Key question (as always): Will our solution scale?
Answer: No! (At least not the first things we examine)
When network depicted as graph, node can be a switch, router, or network. In our text, they think of the nodes as routers, which is a fine abstraction for what we’re doing.
Text talks of forwarding tables vs routing tables, and why they choose to “decouple” the two. Their distinction is that the info in the forwarding table will typically be derived from the info in a routing table. (I.e. a forwarding table line might have a mapping from the network number to an outgoing interface along with some MAC info, such as ethernet hardware address. Router may have additional info such as statistics on how the info was learned, etc. For me, the big distinction is that a forwarding table is structure to optimize the cost of next hop lookup, where a routing table optimized for the purpose of tracking topology changes.
Remember, the key here (as in many respects) will be “does our solution scale”
Spring 2003
CS 332

4. Overview Network as a Graph Nodes represent networks
Edges represent costs
Factors
static: topology
dynamic: load
Spring 2003
CS 332

5. Terminology
Routing domain: internetwork in which all routers under same administrative control
Small to midsized networks, e.g. university campus
Use intradomain routing protocols (interior gateway protocols (IGP))
Internet uses interdomain routing protocols
Spring 2003
CS 332

6. Distributed Algorithms
Centralized control can kill scalability, reliability
Nodes can only compute routing tables based on local info (i.e. information they possess)
Who needs to know what? When?
Who knows what? When?
Convergence: process of getting consistent routing information to all nodes
Spring 2003
CS 332

7. Distance Vector (RIP, Bellman-Ford)
Each node maintains a set of triples
(Destination, Cost, NextHop)
Exchange updates directly connected neighbors
periodically ( on the order of several seconds)
whenever its table changes (triggered update)(?!)
Each update is a list of pairs:
(Destination, Cost)
Update local table if receive a “better” route
smaller cost
came from next-hop
Refresh existing routes; delete if they time out
Spring 2003
CS 332

8. Example Destination Cost NextHop Sample routing table for node B A 1 A
C C
D C
E A
F A
G A
Spring 2003
CS 332

9. Example of Update F detects that link to G has failed
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
Spring 2003
CS 332

10. Note “distances” need
not be hop counts
Spring 2003
CS 332

11. Convergence Issues Distance vector schemes can converge slowly
A is down
initially, then
comes back
up. Assume
all routers
update simultaneously
via common clock
Spring 2003
CS 332

12. Routing Loops
Can happen because several nodes are updating routing tables concurrently
Ex: The “count to infinity” problem
Initially all lines
are up, then either
A goes down or the
link between A and
B is cut (same thing
to B)
Spring 2003
CS 332

13. Loop-Breaking Heuristics
Set infinity to 16
Split horizon
Don’t send routes learned from a given neighbor back to that neighbor
Split horizon with poison reverse
Reply to neighbor but give negative info (such as infinite cost)
These are Hacks!
Spring 2003
CS 332

14. Even Split Horizon Can Fail…
Spring 2003
CS 332

15. Even Split Horizon Can Fail…
Initially, A, B both have distance 2 to D, C has distance 1 there.
Line CD goes down
Both A and B report to C that they cannot get to D
C concludes that D is unreachable and reports this to A and B.
A hears that B has a path of length 2 to D, so it assumes it can get to D in 3 hops. Similarly B assumes it can get to D in 3 hops.
On next exchange they set distance to 4, etc.
Spring 2003
CS 332

16. Distance Vector Summary
Good
Only need communicate with neighbors (so little bandwidth is wasted on protocol overhead)
Relatively little processing of info
Bad
Count to infinity problem
Slow convergence (the real issue)
Despite this, RIP is popular
Because included in original BSD implementation
Spring 2003
CS 332

17. Link-State (OSPF) Strategy Link State Packet (LSP)
send to all nodes (not just neighbors) information about directly connected links (not entire routing table)
Link State Packet (LSP)
id of the node that created the LSP
cost of the link to each directly connected neighbor
sequence number (SEQNO)
time-to-live (TTL) for this packet
Spring 2003
CS 332

18. Link-State (cont) Reliable flooding
store most recent LSP from each node
forward LSP to all nodes but one that sent it
generate new LSP periodically
increment SEQNO
start SEQNO at 0 when reboot
decrement TTL of each stored LSP
discard when TTL=0
Spring 2003
CS 332

19. Route Calculation Dijkstra’s shortest path algorithm Let
N denotes set of nodes in the graph
l (i, j) denotes non-negative cost (weight) for edge (i, j)
s denotes this node
M denotes the set of nodes incorporated so far
C(n) denotes cost of the path from s to node n
M = {s}
for each n in N - {s}
C(n) = l(s, n)
while (N != M)
M = M union {w} such that C(w)
is the minimum for all w in (N - M)
for each n in (N - M)
C(n) = MIN(C(n), C (w) + l(w, n ))
Since algorithms
is a pre-req for
this, I assume
y’all know
Dijkstra’s
algorithm
Spring 2003
CS 332

20. Example
Hi there
Spring 2003
CS 332

21. Link-State Summary Good Bad Converges relatively quickly
Lots of information stored at each node because LSP for each node in network must be stored at each node (scalability problem)
Flooding of LSPs uses bandwidth
Potential security issue (if false LSP propogates)
Spring 2003
CS 332

22. Open Shortest Path First (OSPF)
Typical link-state but with enhancements
Authentication of routing messages
Additional hierarchy (to help with scalability)
Load balancing
Spring 2003
CS 332

23. Distance Vector vs Link-state
Key philosophical difference
Distance vector talks only to directly connected neighbors and tells them what is has learned
Link-state talks to everybody, but only tells them what it knows
Spring 2003
CS 332

24. Metrics Original ARPANET metric New ARPANET metric Fine Tuning
measures number of packets enqueued on each link
took neither latency or bandwidth into consideration
New ARPANET metric
stamp each incoming packet with its arrival time (AT)
record departure time (DT)
when link-level ACK arrives, compute
Delay = (DT - AT) + Transmit + Latency
if timeout, reset DT to departure time for retransmission
link cost = average delay over some time period
Fine Tuning
compressed dynamic range
replaced dynamic with link utilization
Spring 2003
CS 332