CSE 550 Computer Network Design

1. CSE 550Computer Network Design Dr. Mohammed H. Sqalli COE, KFUPM Spring 2007 (Term 062)

2. Outline • Topology Design for Centralized Networks • Multipoint Line Topology • Terminal Assignment • Concentrator Location Lecture Notes - 5

3. Center High speed lines Concentrator Concentrator Low speed lines Terminal Terminal Terminal Terminal Centralized Network Design • Centralized network: is where all communication is to and from a single central site • The “central site” is capable of making routing decisions → Tree topology provides only one path through the center (For reliability, lines between other sites can be included) Lecture Notes - 5

4. Centralized Network Design Problems • Multipoint line topology:selection of links connecting terminals to concentrators or directly to the center • Terminal assignment:association of terminals with specific concentrators • Concentrator location: deciding where to place concentrators, and whether or not to use them at all Lecture Notes - 5

5. Multipoint Line Topology

6. The Greedy Algorithm • At each stage, select the shortest edge possible (myopic or near-sighted): • Start with empty solution s • While elements exist • Find e, the best element not yet considered • If adding e to s is feasible, add it; if not, discard it • May not find a feasible solution when one exists • Efficient and simple to implement → widely used • Basis of other more complex and effective algorithms • A Minimum Spanning Tree (MST) is a tree of minimum total cost • In the case of MST, the greedy algorithm guarantees both optimality and reasonable computationalcomplexity Lecture Notes - 5

7. Constrained/Capacitated MST (CMST) • CMST Problem: • Given: • A central node N0 • A set of other nodes (N1, N2, …, Nn) • A set of weights (W1, W2, …, Wn) for each node • The capacity of each link Wmax • A cost matrix Cij = Cost(i,j) • Find a set of trees T1, T2, …, Tk such that: • Each Ni belongs to exactly one Tj, and • Each Tj contains N0 is minimized Lecture Notes - 5

8. CMST • Objective:Find a tree of minimum cost and which satisfies a number of constraints such as: • Flow over a link • Number of ports • The CMST problem is NP-hard (i.e., cannot be solved in polynomial time) → Resort to heuristics (approximate algorithms) • These heuristics will attempt to find a good feasible solution, not necessarily the best, that: • Minimizes the cost • Satisfies all the constraints • Well-known heuristics: • Kruskal • Prim • Esau-Williams Lecture Notes - 5

9. Kruskal’s Algorithm for CMST • Example:Given a network with five nodes, labelled 1 to 5, and characterized by the following cost matrix • Node 1 is the central backbone node • fmax=5, f1=0, f2=2, f3=3, f4=2, f5=1 Lecture Notes - 5

10. Prim’s Algorithm for CMST Lecture Notes - 5

11. Esau-Williams Algorithm for CMST • Node 1 is the central node. • tij: is the tradeoff of connecting i to j or i directly to the root • If (tij< 0) → better to connect i to j • If (tij≥ 0) → better to connect i directly to the root • Algorithm: Lecture Notes - 5

12. Terminal Assignment

13. Problem Statement • Terminal Assignment:Association of terminals with specific concentrators • Given: • Tterminals (stations) i = 1, 2, …, T • C concentrators (hubs/switches) j = 1, 2, …, C • Cij: cost of connecting terminal i to concentrator j • Wj: capacity of concentrator j • Assume that terminal i requires Wi units of a concentrator capacity • Assume that the cost of all concentrators is the same • xij = 1; if terminal i is assigned to concentrator j • xij = 0; otherwise • Objective: • Minimize: • Subject to: i = 1, 2, …, T (Each terminal associated with one Concentrator) j = 1, 2, …, C (Capacity of concentrators is not exceeded) Lecture Notes - 5

14. Assignment Problem • Given a cost matrix: • One column per concentrator • One row per terminal • Assume that: • Weight of each terminal is 1 (i.e., each terminal consumes exactly one unit of concentrator capacity) • A concentrator has a capacity of W terminals (e.g., number of ports) • A feasible solution exists iffT ≤ W * C Lecture Notes - 5

15. Augmenting Path Algorithm • It is based on the following observations: • Ideally, every terminal is assigned to the nearest concentrator • Terminals on concentrators that are full are moved only to make room for another terminal that would cause a higher overall cost if assigned to another concentrator • An optimal partial solution with k+1 terminals can be found by finding the least expensive way of adding the (k+1)th terminal to the k terminal solution Lecture Notes - 5

16. Augmenting Path Algorithm • Initially, try to associate each terminal to its nearest concentrator • If successful in assigningall terminals without violating capacity constraints, then stop (i.e., an optimal solution is found) • Else, • Repeat • Build a compressed auxiliary graph • Find an optimal augmentation • Until all terminals are assigned Lecture Notes - 5

17. Building a Compressed Auxiliary Graph • U: set of unassociated terminals • T(Y): set of terminals associated with Y Lecture Notes - 5

18. Building a Compressed Auxiliary Graph Lecture Notes - 5

19. Example • Cost Matrix: • W = 2 (capacity of each concentrator) • Solution: (a, H) (b, I), (c, H), (d, G), (e, I), (f, G) • Cost = 15 Lecture Notes - 5

20. Concentrator Location

21. Problem Statement • Concentrator location: deciding where to place concentrators, and whether or not to use them at all • Given: • Tterminals (stations) i = 1, 2, …, T • C concentrators (hubs/switches) j = 1, 2, …, C • Cij: cost of connecting terminal i to concentrator j • dj: cost of placing a concentrator at location j (i.e., cost of opening a location j) • Kj: maximum capacity (of terminals) that can be handled at possible location j • Assume that terminal i requires Wi units of a concentrator capacity • xij = 1; if terminal i is assigned to concentrator j; 0, otherwise • yj = 1; if a concentrator is decided to be located at site j; 0, otherwise • Objective: • Minimize: • Subject to: i = 1, 2, …, T (Each terminal associated with one Concentrator) j = 1, 2, …, C (Capacity of concentrators is not exceeded) Lecture Notes - 5

22. Add Algorithm • Greedy Algorithm • Start with all terminals connected directly to the center • Evaluate the savings obtainable by adding a concentrator at each site • Greedily select the concentrator which saves the most money Lecture Notes - 5

23. Add Algorithm • Initialization: • M: set of locations • Select an initial location m • Assume all terminals are connected to m • Set L0 = {m} • Set k = 0 (iteration count) • c'i = cim, i= 1, 2, …, N • Compute: • For , do where • Determine a new m such that: If there is no such m, go to step 4. • Update: and for Set and k:= k+1, and go to Step 1. • No more improvement possible; stop. Lecture Notes - 5

24. Example • Cost Matrix: • Kj = 3, j = 1, 2, 3, 4 (capacity of each concentrator) • d1 = 0, d2 = d3 = d4 = 2 • Solution:S2 (1, 2, 3), S1 (4), S4 (5, 6) • Cost = 9 Lecture Notes - 5

25. References • A. Kershenbaum, “Telecommunications Network Design Algorithms”, McGraw-Hill,1993 • M. Pióro and D. Medhi, “Routing, Flow, and Capacity Design in Communication and Computer Networks”, Morgan Kaufmann Publishers, Inc., 2004 Lecture Notes - 5