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ISP and Egress Path Selection for Multihomed Networks Amogh Dhamdhere, Constantine Dovrolis

ISP and Egress Path Selection for Multihomed Networks Amogh Dhamdhere, Constantine Dovrolis Networking and Telecommunications Group Georgia Institute of Technology. Presented by Karl Deng. Problem Definition. Provision the multihoming configuration of a source network S. Inputs: S

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ISP and Egress Path Selection for Multihomed Networks Amogh Dhamdhere, Constantine Dovrolis

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  1. ISP and Egress Path Selection for Multihomed Networks Amogh Dhamdhere, Constantine Dovrolis Networking and Telecommunications Group Georgia Institute of Technology Presented by Karl Deng

  2. Problem Definition Provision the multihoming configuration of a source network S. Inputs: S D = {Di} (i = 1 .. M) R = {ri} K

  3. Two Phases • Phase I - ISP Selection • Select K ISPs that S will subscribe to. • Objectives: Optimize monetary cost and availability. • Phase II - Egress Path Selection • Determine the ISP that should be used to reach each of the M major destinations. • Objectives: Select congestion-free paths and minimize cost. (Avoid long-term congestion.) Phase-I can be repeated in long time scales, from weeks to months, while Phase-II can be repeated whenever there is a major change in the egress traffic distribution.

  4. - the set of possible ISPs to which S can subscribe • Select K ISPs out of by taking into account of the following three factors: • Monetary cost • AS-level path length • Path diversity For example, if = 15 and K = 4, = 1365 Phase I - ISP Selection possible selections  Exhaustive search

  5. Exhaustive search - the set of all possible combinations of K ISPs from the set - optimal combination - total cost by taking into account of all three factors

  6. Calculate the cost for each combination - total cost - monetary cost - cost associated with the AS-level path length - cost associated with path diversity -- normalization factors

  7. Monetary cost Constraint:Tj < A - pricing function of ISP j • G - a mapping between ISP and destination • e.g., j = G(i), map destination i to ISP j • Tjdepends on G NP-hard and KMpossible ways of mapping  Heuristic (FFD-like algorithm)

  8. Algorithm-1: a FFD-like algorithm FFD - First Fit Decreasing Basic idea: Start with the largest destination, in terms of traffic rate, and route it through the lowest-cost ISP. It is possible that Algorithm 1 will fail to find a feasible mapping (due to the capacity constraint).

  9. Cost associated with the path length Constraint:Tj < A pj(i) - AS-level path length to reach a destination i through ISP j. Similar to the monetary cost problem, also use Algorithm-1.

  10. number of K-shared links to destination i through the ISPs in • - a path diversity metric; indicates the resiliency to single inter-AS link failures Cost associated with the path diversity

  11. Looking Glass Servers (LGS) LGSs are routers inside an ISP that report AS-level paths to given destination networks. Most ISPs maintain public Looking Glass Servers We assume that each ISP in has a LGS from which S can determine the AS-level paths to destinations in D.

  12. Phase II - Egress Path Selection Given the K ISPs selected by Phase I, determine an optimal destination-ISP mapping. Constraint: None of the paths to the destinations in D is congested. Difficulty: Available bandwidth of the upstream network paths is generally unknown.  We cannot know a priori whether a given mapping will be congestion-free or not  Iterative routing approach

  13. Iterative Routing Approach S routes its egress traffic based on a certain mapping for some time while measuring the loss rate in the corresponding paths. If any of these paths is congested the traffic is rerouted based on a different mapping. We allow a certain cost increase while trying to keep the amount of rerouted and dropped traffic as low as possible. A two-step algorithm.

  14. A Two-step Algorithm 1. Initial mapping • Assume that bottlenecks of all paths locate at the K access links. • Calculate the minimum-cost mapping  Algorithm-1 2. Stochastic search • Find a congestion-free mapping in the vicinity of the minimum-cost mapping • Simulated annealing

  15. Reroute a single congested flow at a time • Allocate the “max-loss” destination to the ISP that will result in the minimum cost increase • Two additional termination Conditions: • If monetary cost is too large. • If the congestion cost has not decreased significantly over a number of iterations. Algorithm-2: Simulated Annealing • Starts with an initial mapping G and an initial temperature T. • Route traffic as in mapping G. • ccurr = cost(G) • repeat • if ccurr = 0 then • return G {congestion-free solution} • else • Generate new mapping Gnew • Route traffic as in mapping Gnew • cnew = cost(Gnew) • if cnew ≤ ccurr then • Accept Gnew (i.e., G= Gnew, curr=cnew) • else • Accept Gnew with probability e−(cnew−ccurr)/T • end if • T = ρT {cooling rate} • end if • until T ≈ 0

  16. Summary • Phase I - ISP Selection • Select K ISPs. • To minimize: • Monetary cost - Algorithm 1 (FFD-like heuristic) • Cost associated with AS-level path length - Algorithm 1 • Cost associated with Path diversity • Phase II - Egress Path Selection • Determine the destination-ISP mapping. • Two-step Algorithm: • Initial mapping - Algorithm 1 • Stochastic search - Algorithm 2(Simulated Annealing)

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