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Chapter 5. Routing Algorithm in Networks. Routing Algorithm in Networks. How are message routed from origin to destination? Circuit-Switching → telephone net. Dedicated bandwidth (path) Message-switching : using routing table share link bandwidth concept, 同一 msg 用同一 path

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chapter 5

Chapter 5

Routing Algorithm in Networks

routing algorithm in networks
Routing Algorithm in Networks
  • How are message routed from origin to destination?
    • Circuit-Switching → telephone net. Dedicated bandwidth (path)
    • Message-switching : using routing table share link bandwidth concept, 同一msg用同一path
    • Packet-switching : 不同路徑for every packet out of order.
routing algorithm in networks1
Routing Algorithm in Networks
  • Virtual circuit
    • – msg delivered in the order transmitted
    • – sharing, i.e. no dedicated paths.
  • Implementation
    • centralized ─
    • distributed

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Out of order

Re-assembly problem

Vulnerable to failure

Comm. Of control infomation

shortest path algorithm graph
Shortest Path Algorithm -Graph
  • G = (V, A)
  • dij is the src weight of (i, j) A.
  • dij =  if (i, j) A.
  • Source node is node 1

# of nodes

|V|=N

Set of

nodes

Set of

Direct arcs

5 2 3 shortest path
§5.2.3 Shortest Path
  • P.396 §Bellman-Ford Alg. (can handle negative weights but not negative cycles)
  • Let Di(h) be the length of a shortest path from 1 to i using h or fewer arcs (or links)
  • Initially D1(0) = 0

Di(0) = , i  1

  • For each h = 0, 1, 2,…, N-2
5 2 3 shortest path1
§5.2.3 Shortest Path
  • [Thm] : The alg. Finds the correct shortest path lengths proof by induction.

are the lengths of the shortest path using 1 or fewer links.

5 2 3 shortest path2
§5.2.3 Shortest Path
  • Induction step : suppose Di(h) are the correct length of shortest paths using h or fewer links.

k1

dk1i

dk2i

k1

i

dk3i

k1

5 2 3 shortest path3
§5.2.3 Shortest Path
  • Complexity:
    • The alg. Requires at most N-1 iterations.
    • For each iteration, the recursion is performed by N-1 nodes.
    • Each application of the recursion requires no more than N-1 addition & comparisonsO(N3) complexity.

Example : see P.397. Fig 5.31

dijkstra s algorithm position arc weights only
§Dijkstra’s Algorithm.(position arc weights only.)
  • Let p be the set of nodes that are permanently labeled.
  • Step 0 : set P={1}, D1 = 0, and Dj = d1j, j  1
  • Step 1 : Find i  P, s.t.
dijkstra s algorithm position arc weights only2
§Dijkstra’s Algorithm.(position arc weights only.)
  • After k iterations of the algorithm. P contains the k+1 nodes that are closest to node 1 and their permanent labels are the correct shortest path lengths from node 1.

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dijkstra s algorithm position arc weights only3
§Dijkstra’s Algorithm.(position arc weights only.)
  • Proof by inductions:
  • True for k=1:
  • Inductive step: suppose statement is true for any k. P={1, i1,i2,…,ik} are (k+1) closest nodes to 1. Dk is length of the shortest path from 1 to k, kP.
dijkstra s algorithm position arc weights only5
§Dijkstra’s Algorithm.(position arc weights only.)
  • By construction, ik+1 is at least as close to 1 as any other node in
  • Complexity:
    • The algorithm requires N-1 iterations.
    • For each iteration:
      • Step 1 : requires at most N comparison
      • Step 2 : requires at most N addition & comparisons.

complexity is O(N2)

floyd warshall algorithm
§Floyd-Warshall algorithm
  • Finds shortest path between every pair of nodes

O(N3)

  • Negative arc weights(but no negative cycle)

Note: every pair

Bellman-Ford O(N4)

Dijkstra O(N3)

floyd warshall algorithm1
§Floyd-Warshall algorithm
  • Dij(n) = shortest path length between nodes I and j using only nodes 1,2,3…n as intermediate nodes on paths.

Initially, Dij(0) = dij

For n=0,1,2,…,N-1

Dij(n+1) = min[Dij(n+1) , Di,n+1(n+1)+dn+1,j ]

for all i  j

floyd warshall algorithm2
§Floyd-Warshall algorithm
  • Example see fig.?
  • Proof by induction :
  • Complexity :
    • N iterations
    • Each iteration require N(N-1) comparisons & additions

O(N3)

centralized synchronous bellmen ford algorithm
Centralized, synchronous Bellmen-Ford Algorithm
  • Let Di(h) be the shortest (<=h) path length from node 1 to node I
  • Initially, D1(h) = 0 for all h

Di(0) =  for i = 1

centralized synchronous bellmen ford algorithm1
Centralized, synchronous Bellmen-Ford Algorithm
  • => h is an index for iteration # (the # of links allowed in paths) ← synchronous
  • => 有困難 h 必須 all the same
5 2 4 distributed asynchronous bellman ford algorithm
§ 5.2.4 Distributed Asynchronous Bellman-Ford Algorithm
  • Let Di be the shortest path length from node i to 1.
  • Let N(i) = { j|(i,j)  A }
5 2 4 distributed asynchronous bellman ford algorithm1
§ 5.2.4 Distributed Asynchronous Bellman-Ford Algorithm
  • Let Di be the shortest path length from node i to 1.
  • Let N(i) = { j|(i,j)  A }

Neighbor

  • At each time t, each node i  1 has available:
  • Dji(t) : i’s latest estimate (sent by node i) of the shortest distance from node j  N(i) to node 1.
  • Di(t) : i’s latest estimate (computed by node i) of the shortest distance from node i to 1
5 2 4 distributed asynchronous bellman ford algorithm2
§ 5.2.4 Distributed Asynchronous Bellman-Ford Algorithm
  • At each point in time, each node. i  1 is doing one of the followings :
    • Node i update Di(t) by

And leaves the estimate Dji(t), j  N(i) unchanged & sends Di, j  N(i)

    • Node i receives from one or more neighbors Di , j  N(i) computed by j at an earlier time.

Node i update Dji(t) and leaves other estimate unchanged.

    • Node i is idle.
5 2 4 distributed asynchronous bellman ford algorithm3
§ 5.2.4 Distributed Asynchronous Bellman-Ford Algorithm
  • Prove convergence of the distributed asyn. B-F Alg.
  • Let Ti be the set of times at which node i update Di(t)
  • Let Tji be the set of times at which node i update Dj(t)
  • Let {t0, t1…} be the ordering of
5 2 4 distributed asynchronous bellman ford algorithm4
§ 5.2.4 Distributed Asynchronous Bellman-Ford Algorithm
  • Assumptions:
    • nodes never stop updateing their estimate Di(t) and receiving msg. from all their neighbors Dji(t), j  N(i), Ti, Tji have  # of elements.
    • All estimates Di(t), Dji(t), i  V, j  N(i) are non-negative.
    • Old distance information is eventually purged from the system.
5 2 4 distributed asynchronous bellman ford algorithm5
§ 5.2.4 Distributed Asynchronous Bellman-Ford Algorithm
  • Let Dik be the k-th iteration of Bellman-Ford (k=0,1,2…) for node i = 1,2,…N when
  • Let Dik be the k-th iteration of Bellman-Ford when Di(0) = 0, i = 1,2,…N
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