1. CP3397 Network Design and Security Lecture 4 WAN design - Principles and practice

2. WAN design Wide area networks constructed from private circuits (leased lines) need careful design to optimise performance minimise costs provide adequate service allow redundancy for fault tolerance

3. Backbone and access networks WANs can be split into two parts Backbone network linking main centres Access networks linking endpoints to nearest backbone node Both aspects of the network need design Different rules apply to each type

4. Principles 1. Good designs have many well-utilized components 2. In voice network utilization should be high (to reduce costs) - in a data network utilization should be low (to reduce delay) 3. Aim for 50% utilization on all links (to balance cost/delay) 4. Aim to have as few links as possible under 50% utilization 5. Use “natural traffic centres” - found from “weight” calculation 6. Balance need for shortest path, economy of scale from high speed links and utilization 7. Most design algorithms need repeated application to give best results

5. Some definitions Graphs n A,B,C etc. are vertices(nodes) n (A,X), (X,Y) etc. are edges n P,Q,Z is a cycle (loop) n Degree of a node is the number of edges at the node –Degree Y =3, degree C=1 X Y Z P Q A B D C

6. Trees A tree is a connected simple graph with no cycles e.g. X Y Z P Q A B D C

7. Star A tree is a star if only 1 node has degree >1 X Y Z P Q A B D C

8. Chains A chain is a tree with no nodes of degree >2 X Y Z P Q A B D C

9. Weighted graphs Each edge has a value (e.g. link speed, cost, etc.) Weight of the edge e i = w(e i ) To optimise a connected graph find the graph with the minimum weight The Minimal Spanning Tree (MST)

10. Finding the MST Two algorithms Prim and Kruskal Prim starts by selecting a node, adding the “least expensive edge” iterates until tree is built Kruskal achieves the MST by starting with a graph and cutting out edges

11. Example MST

12. Use of MSTs Small design problems - few nodes Highly reliable links with low “downtime” or network can tolerate unreliability Nodes ‘v’ reliability As the number of nodes increases reliability decreases (exponentially!)

13. Shortest path trees SPTs are when the path between each pair of nodes has the lowest weight Can be found using Dijkstra’s algorithm  See Cahn p67 and Kenyon p102 MSTs and SPTs will be different Prim and Dijkstra algorithms can be combined to give MST or SPT using parameter alpha Ref: R. S. Cahn (1998) “Wide area network design”, Morgan Kaufmann, ISBN 1-55860-458-8 and the Delite design tool, http://www.mkp.com/wand.htm and mirrored on the CP3397 homepage Ref: T Kenyon (2002) “High-performance data network design” Digital Press, ISBN 1-55558-207-9

14. Access design Each node on a backbone may have a number of local access points Access networks route all traffic to the local backbone node. With n nodes there are n n-2 spanning trees! There are algorithms that reduce the possibilities - e.g. Esau-Williams, Sharma (Cahn Chapter 5)

15. Backbone design Aim is to minimise the degree of the nodes (X) (i.e. number of connections at each) and minimise the number of hops (H) between all the nodes These need balancing to produce a credible design Fully-connected n-node mesh (H=1, X=n) Star (H=2, X=1 for all except central node)

16. Design algorithm Mentor algorithm (one of many) relies on radius and weight to determine backbone  radius is proximity-based  weight is traffic-related (in and out) Calculates merit of a site based on  distance from centre of network  weight - i.e. traffic

17. Algorithm steps 1. Cluster sites within radius (Rparm) around those with largest merit 2. Select backbone centre (smallest weight x distance) 3. Build backbone tree 4. Find the sequence of all pairs on the tree starting with the outside 5. Choose homes (sites between each pair)

18. Algorithm steps 6. Consider each pair once and add links if  utilization is too high  otherwise traffic is sent via home node  This adds links between non-adjacent sites

19. Example

20. Detail diagrams South East Midlands

21. Input data Coordinates of sites Cost of links Traffic between sites in kbytes User population at each site Parameters Rparm, wparm, slack

22. Network design Parameters Rparm 0.4 Wparm 1.0 Slack 0.0 Utilization 0.5 Cost 757991

23. Alternative robust design Parameters Rparm 0.4 Wparm 1.0 Slack 0.5 Utilization 0.5 Cost 766717

24. Detail diagrams Midlands area detail512K 128K 3x 128k

25. Summary Design will depend on traffic, costs, and Other desirable parameters such as slack and utilization Design methods for Access and backbone are different Many algorithms are available Real networks will require careful cost minimisation