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Chapter 11

Chapter 11. Network Redesign. Overview. Great success in the previous chapters : designed a network that will carry the traffic for a real life design problem. What if traffic changes? ( and not only traffic) - departments grow, people move, new applications come on-line

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Chapter 11

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  1. Chapter 11 Network Redesign EMIS 8392 Maya Petkova

  2. Overview • Great success in the previous chapters : designed a network that will carry the traffic for a real life design problem. • What if traffic changes? ( and not only traffic) - departments grow, people move, new applications come on-line - A Industries acquires B Inc. ( merge) - C Ltd. Spins off D Enterprises ( new networks) • More work for the designers and new approaches to solving design problems. EMIS 8392 Maya Petkova

  3. Overview – Cont’d • Methods and algorithms in Ch. 11 cover an increasing mismatch between the original network and the new requirements. • “Network growth is like growth in the family”. • Example: designed a network that handles 500 Kbps of data traffic. Traffic grows to 600 Kbps ( fiddle with the routing or might add capacity to heavily used links). Next growth spurt ( new locations, new staff) and traffic grows to 725 Kbps ( link the new locations to the existing backbone, add capacity to a few additional links). Next a rival comes and we have to merge 2 separate networks - moment to REDESIGN. • PROBLEM : when to redesign when no single large event acts as a trigger? EMIS 8392 Maya Petkova

  4. Calculating the Payback Period • Cost of upgrading a network compared with a complete new design. • Compare the payback periods for 650 Kbps traffic and 1.25 Mbps traffic. EMIS 8392 Maya Petkova

  5. Calculating the Payback Period - Cont’d Q. What info do the network designers need to know in order to be able to carry out the business analysis in the Table on slide 4? A. They need the traffic and the costs. • Optimal cost can be calculated by a variant of Mentor. • Incremental cost can not be calculated by the algorithms we already have at hand. • Need new class of algorithms that takes the existing network topology as an input not just the traffic and the link costs. EMIS 8392 Maya Petkova

  6. Incremental Design Problem() • Produce the minimal-cost change to the network that will bring it back to an operating point where we feel comfortable. • Capacity assignment problem is one way of solving • Drawback – CAP tries to provide a better fit if the initial network is close to what is needed. It does not do a good job if the network and the traffic are out of kilter. EMIS 8392 Maya Petkova

  7. A Tutorial Introduction to the Three basic approaches to incremental design: • Routing changes. • Link resizing. • Link addition. Above is the order in which they should be considered. EMIS 8392 Maya Petkova

  8. Routing Changes • Main advantage – no additional cost ( no circuits to order, no equipment to acquire). • No delay in implementing them. • Main disadvantage – only work with certain routing algorithms and with relatively small changes in traffic. • With larger changes in traffic rerouting attempts will fail. ( if we use min. hop routing no point in even considering routing changes). • The more the network is able to control the flow, the more we want to consider rerouting before redesign. EMIS 8392 Maya Petkova

  9. Example - Routing Changes Each link has capacity 4. We can split or bifurcate requirements. Traffic AH = 2 .Traffic between adjacent nodes is 1. Initial routing - split the AH traffic so half flows along the top of the network and half flows along the bottom. A B C D E F G H EMIS 8392 Maya Petkova

  10. Example –Routing Changes - Cont’d Traffic grows with BC = 3, AH = 3. Initial routing not feasible - BC will be saturated by 4.5 units of flow. We have 7 units of flow across (BC, FG) cut which has capacity of 8. We want a routing that carries a flow of 3.5 on both BC and FG. A B C D E F G H EMIS 8392 Maya Petkova

  11. Example - Link Resizing Traffic grows with BC = 3, AH = 3, AC = 2. We have saturated the (BC, FG) cut. No clever routing can fix this problem. Need to add capacity. The cheapest solution double the capacity of the BC link. A B C D E F G H EMIS 8392 Maya Petkova

  12. Example - Link Addition Traffic grows with BC = 3, AH = 6, AC = 3, FG = 2. We have saturated the (BC, FG) cut again. We choose to add a new link AH of capacity 4. Therefore new cut (BC, FG, AH) of capacity 16. Add 3 units of flow on AH link – the remaining flow can be distributed as before. A B C D E F G H EMIS 8392 Maya Petkova

  13. Network Cost as a Function of the Total Traffic Traffic ( Mbps ) EMIS 8392 Maya Petkova

  14. Design Principle 11.1 The fundamental thing to understand about any network is how much traffic it can carry before it breaks. This process is called sensitivity analysis. • “Break” - unacceptable blockage or delay, depending on the network type. • Cost deferral rather than cost avoidance –different prospective of network optimization. • If we build a network for a stable user base with fixed usage – build the cheapest. • If we build for a rapidly changing concern we may overbuild initially to defer the upgrade costs. EMIS 8392 Maya Petkova

  15. The Rerouting Algorithm There are 2 principal purposes for rerouting algorithms. 1.The actual traffic on a network is different than what was planned 2.The network traffic is growing and we want to eke out few more months before redesign. What can be done depends critically on the network layer of the network we are using. EMIS 8392 Maya Petkova

  16. The Rerouting Algorithm - Cont’d • RIP routing – nothing can be done • FSMH routing – the network will try to deal with congestion by finding alternate routes (we could help by putting traffic in another order, still beyond control) • If network allows arbitrary bifurcation there exists elegant math approach developed by Bertsekas and Gallager. Produces optimal routing for the traffic, but is beyond the ability of most commercial switches and routers. EMIS 8392 Maya Petkova

  17. The Rerouting Algorithm - Cont’d • Focus on minimum distance routing since it is one of the most widely used routing schemes. • Suppose we designed a network with link utilization below 50%. If 2 months after going on-line a link reaches 52% utilization we do not want to go back to a full-scale redesign. • “Newton’s first low of networks”:A network that is working tends to continue working; a network that is not working tends to continue not to work. • A stable slightly congested network is always preferable to an unstable, uncongested network. EMIS 8392 Maya Petkova

  18. When to Redesign • The policy issues involving redesign are quite interesting but impossible to quantify. • The network passes the threshold between slightly congested and significantly congested at some point. Network users, mangers and owners decide where that transition occurs. • Generally: we design a network with a maximum utilization u ( or a blocking level b ). We don’t undertake any redesign until the utilization reaches level u’ (or b’ ) EMIS 8392 Maya Petkova

  19. The Balancing Algorithm • Assume some link has reached level u’. Examining the network we find that there are n links with utilization over u. Should we insist that the network be returned to a max utilization of u? • The balancing algorithm starts with a network at one level of utilization and produces a new network at another, lower level of utilization. It will try to reduce congestion by rerouting. If it is not possible it will fail and leave it to another algorithm like capacity assignment to upgrade the links. The goal is to reduce the utilization on all links below EMIS 8392 Maya Petkova

  20. The Balancing Algorithm – Cont’d • Uses ISP ( incremental shortest-path ) algorithm used in MENTOR II to decide whether or not to add a direct link. • ISP : – is there an a length we can give a direct link with which it will attract an amount of traffic that will justify the link - goal is to identify all the pairs that could use a link in place of the current path. EMIS 8392 Maya Petkova

  21. The Balancing Algorithm – Cont’d • Denote the initial network N. N is a directed graph, each link is represented by 2 directed edges, 1 in each direction. • After loading the traffic a link can be in 1 of 3 stages : - uncongested in both directions - congested in 1 direction, but not in the other - congested in both directions • The goal is to remove the congestion by changing the link lengths and not by adding capacity. EMIS 8392 Maya Petkova

  22. The Balancing Algorithm – Steps • Divide the links into mesh links and tree links. Links AB, YZ, QR, RS, ST are called tree links. There is no alternate route between the endpoints. The rest of the links are mesh links. S A Q R B T C Z EMIS 8392 Maya Petkova

  23. The Balancing Algorithm – Steps - Cont’d If Any of the tree links has a utilization of greater than then the algorithm returns FAIL. 2. Consider only the subgraph of mesh links N’ N. N’ = N \ { A, R, S, T, Z }. Such a network is called reduced network. 3. Collapse the traffic onto the reduced subnetwork. If we had traffic form A to T in the original network it becomes traffic from B to Q in N’. If we had traffic form Q to T it does not enter the subnetwork. Let , n = |O|. If n= 0, return. . EMIS 8392 Maya Petkova

  24. The Balancing Algorithm – Steps - Cont’d 4. Divide the directed links into 3 categories: - overutilized : utilization > = - underutilized: utilization < = , where is a parameter passed to the algorithm - the rest of the links have utilization u: < u < 5. Try to reduce the number of links with utilization greater than by changing the lengths of overutilized and underutilized links. EMIS 8392 Maya Petkova

  25. The Balancing Algorithm – Steps - Cont’d Let n is the number of overutilized links. • Sort the overutilized links in decreasing order of utilization • Loop over the overutilized links. - A link L initially has length len. Use ISP to compute candidate lengths for L : At each new length the link will move some of its traffic to an alternate route not using L. EMIS 8392 Maya Petkova

  26. The Balancing Algorithm – Steps - Cont’d • Loop over the candidate lengths. - Set len(L) to each to each candidate length - Compute : , let n’ =|O’|. - If n’ < n then set len(L) = , n = n’ and break from the inner loop. • If n = 0 return SUCCESS. • Sort the underutilized links in increasing order of utilization. • Loop over the underutilized links. EMIS 8392 Maya Petkova

  27. The Balancing Algorithm – Steps - Cont’d - A link L initially has length len. Use ISP to compute candidate lengths for L : - At each length the link will attract some traffic from another route not using L. • Loop over the candidate lengths. - Set len(L) to each to each candidate length - Compute : , let n’ =|O’|. - If n’ < n then set len(L) = , n = n’ and break from the inner loop. EMIS 8392 Maya Petkova

  28. The Balancing Algorithm – Steps - Cont’d • If n = 0, return SUCCESS. If n is lower than the previous round loop through the links again. 6. At this point we have been unable to reduce the number of links with utilization of greater than to 0, return FAILURE. EMIS 8392 Maya Petkova

  29. The Effectiveness of the Balancing Algorithm • Hard to measure ( interested in the behavior of the algorithm on a network that has been carefully optimized to meet the previous traffic). • To test : take an optimized mesh network of n nodes and grow the traffic. Can see the maximum congestion on links using the default loading and then using the link lengths provided by the balancing algorithm. • To grow the traffic: several ways implemented as randreq3.c. Can be viewed as incremental traffic generators EMIS 8392 Maya Petkova

  30. The Effectiveness of the Balancing Algorithm – Cont’d • Uniform traffic growth, each existing piece of traffic Traf[i][j] becomes • Node-based growth. Each site is given a growth rate. Increase the traffic between site i and site j by • Average growth. Each existing piece of traffic is multiplied by where rand() is a pseudorandom number generator uniformly distributed in [0,1]. EMIS 8392 Maya Petkova

  31. The Effectiveness of the Balancing Algorithm – Cont’d • Centered growth. We generate additional traffic form a given site to and from all other sites. • Scattered growth generates new traffic that is independent of the previous traffic on the network. Use the population to select the city pairs and then allocate traffic in the range • All generally useful in doing sensitivity analysis and capacity planning. EMIS 8392 Maya Petkova

  32. The Effectiveness of the Balancing Algorithm – Example • Variant of the 50 sites problem with 10-node backbone. Built 2 designs - costing $95,251/month – N507-95k.net( mixed backbone with 56K and 256K links) - costing 96,906/month – N507-96k.net ( only 256K links) • Suppose the initial traffic estimates Traf[i][j] were wrong and the observed traffic was Traf’[i][j]. Assume is uniformly distributed in the range [0.9,1.25] EMIS 8392 Maya Petkova

  33. The Effectiveness of the Balancing Algorithm – Example - Cont’d • If we load the more expensive design with the new traffic – no problem • If we load N507-95 design link form N50 to N48 is loaded 50.9%. • We we have a hard constraint of 50 % utilization, • Collapse the the traffic into the backbone. There are 5 requirements that use the link N50 to N48. EMIS 8392 Maya Petkova

  34. The Effectiveness of the Balancing Algorithm – Example - Cont’d EMIS 8392 Maya Petkova

  35. The Effectiveness of the Balancing Algorithm – Example - Cont’d • Pair (N42, N48) has alternate length 669. • Pair (N43, N48) has alternate length 669. • Pair (N46, N48) has alternate length 669. • Pair (N50, N48) has alternate length 1169. • Pair (N50, N49) has alternate length 71. • The initial length on N50 to N48 in 348. If we lengthen it to 348+71+1 = 420 then the traffic between N50 and N49 will take the 3-hop path via N46 and N45 rather than the 2-hop path via N48. EMIS 8392 Maya Petkova

  36. The Effectiveness of the Balancing Algorithm – Example - Cont’d • The size of the range is quite important – if traffic estimates are uniformly distributed in the range of [0.7, 1.45] we will have different behavior. • In general - the balancing algorithm can not do miracles. • If the congested links are sparse it could do a good job provided that the quantum of traffic is not too large. • If the smallest amount of traffic that moves when we change the routing length of a link is 2% or 5%, we can probably reduce traffic in small steps to the desired level. . EMIS 8392 Maya Petkova

  37. Redesigning for New Traffic • Large change in the traffic - the balancing algorithm will notbe able to restore the network to an acceptable operating point. • Either alter the network or live with a degraded level of service. • Capacity assignment is one approach to altering the network. • The difference between capacity assignment(CA) to reach a performance constraint and capacity assignment for redesign is change in the stopping condition rather than changing the method of selecting which links to upgrade. EMIS 8392 Maya Petkova

  38. Redesigning for New Traffic - Cont’d • CA with performance constraint stopped adding capacity when we reached a preset average packet delay. Paid no attention to the total cost. • Redesigning the network we may or may not have a performance constraint. We are interested in the cost. • Modify the algorithm to stop when we have either reached the performance constraint, say, 100 ms average delay or reached the budget limit, say, $ 50, 000/month. May also wish to stop when we changed certain number of links. EMIS 8392 Maya Petkova

  39. Redesigning for New Traffic - Cont’d • CA gives more flexibility than balancing but does not allow the network to grow naturally. Suppose we design a series of network for total traffic levels: • At each stage designed the best network available with the algorithms in hand. How does the network grow? - sparse with low-speed links – as traffic grows it adds a few direct links – more growth, more direct links – sparse design with higher-speed links EMIS 8392 Maya Petkova

  40. Redesigning for New Traffic - Example Linear network Linear tariff: A+ B x dist for 64 Kbps links 2A + 2B x dist for 256 Kbps links. Traffic between all node pairs is exactly 1Kbps. N1 N2 N10 EMIS 8392 Maya Petkova

  41. Redesigning for New Traffic - Example • Initial design – string of 64 Kbps links. • If there are n nodes on one end of the link and (10-n) nodes on the other end the total traffic over the link in each direction is n x (10-n). EMIS 8392 Maya Petkova

  42. Redesigning for New Traffic - Example • Assume traffic between the node pairs grows to 1.5 Kbps. • Assume traffic doubles – at least 3 links are over 75% utilized. • Difficulty using CA is alone is that there are so many links that are congested. • If we want 50 % utilization - upgrade 5 links, if 66% - upgrade 3 links with more expensive ones. • For the 3 links update what is the cost? • Delay will be low but what if A is large compared to B x dist( Ni, Nj)? • Better approach – enrich the topology. EMIS 8392 Maya Petkova

  43. Redesigning for New Traffic - Example • Suppose we add a single link for N3 to N8 and use it to route all the traffic between (N1, N2, N3) and (N8,N9,N10). • Flow in each direction will be 9x2Kbps = 18 Kbps. • Utilization will be 28 % ( alternative was to add 3 separate links). • If we add the link from N3 to N8 we have reduced the traffic on most heavily used links, brought back the utilization at max 50% , reduced the max number of hops from 9 to 5. • Perfect example of adding link, saving money, adding reliability and decreasing the maximum hops. EMIS 8392 Maya Petkova

  44. Homework # 15 Show that when we add link from N3 to N8 to the chain network from slide 40, we reduce the maximum number of hops to 5 if we give the link an appropriate length. If the length of each chain link is 100, what range of lengths can we give the N3 to N8 link to achieve the 5 hop limit using shortest-path routing? EMIS 8392 Maya Petkova

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