ISP and Egress Path Selection for Multihomed Networks

1. ISP and Egress Path Selection for Multihomed Networks Amogh Dhamdhere Constantine Dovrolis (amogh,dovrolis)@cc.gatech.edu Networking and Telecommunications Group College of Computing Georgia Tech

2. Multihoming • Multihoming: Connection of a stub network to multiple ISPs • 70% of stub networks are multihomed • Redundancy • primary/backup relationships • Load Balancing • Distribute outgoing traffic among ISPs • Cost Effectiveness • Lower cost ISP for bulk traffic, higher cost ISP for performance-sensitive traffic • Performance • Intelligent Route Control Amogh Dhamdhere IEEE Infocom 2006

3. Major Questions • How to select the set of upstream ISPs ? • Low monetary cost • Good performance (low delay, loss rate) • Path diversity to major traffic destinations – improves robustness to network failures • How to allocate egress traffic to the set of selected ISPs ? • Objective: Avoid congestion on the upstream paths • Also maintain low cost Amogh Dhamdhere IEEE Infocom 2006

4. Related Work • Wang et al. (Infocom 2004) • ISP selection to minimize cost to subscriber • Did not consider performance constraints and path diversity • Applicable only to percentile based charging pricing function • IRC systems (RouteScience, Internap, Radware..) • Path switching for better performance • Work on short timescales – Can lead to oscillations • Goldenberg et al. (Sigcomm 2004) • IRC algorithm for optimizing latency and cost over short timescales • Applicable to the percentile based charging model Amogh Dhamdhere IEEE Infocom 2006

5. Problem Definition • Two phase problem • Phase I – ISP Selection: • Select K upstream ISPs • K depends on monetary and performance constraints • “Static” operation • Change only when major changes in the traffic destinations or ISP pricing • Phase II – Egress Path Selection • Allocate egress traffic to selected ISPs • Avoid long term congestion and minimize cost • “Semi-static” operation, performed every few hours or days Amogh Dhamdhere IEEE Infocom 2006

6. Assumptions • We provision only the egress traffic of S • The set of M major destinations is known • Average rates to the major destinations are known • Number of ISPs to choose (K) and the set of possible ISPs (I) is known • An ISP charges based on the volume of traffic routed through it (volume based charging) • Assume increasing and concave pricing functions Amogh Dhamdhere IEEE Infocom 2006

7. Objectives of ISP selection • ISP selection should consider both monetary cost and performance • Minimum monetary cost • Estimate the cost that “would be” incurred if a set of ISPs was selected • Minimum AS-level path lengths • Longer paths can lead to larger delays and increase vulnerability to inter-domain routing failures • AS-level paths can be measured offline using Looking Glass Servers • Maximum Path diversity • AS-level paths to destinations should be as “different” as possible Amogh Dhamdhere IEEE Infocom 2006

8. ISP Selection • K ISPs to be selected out of |I| • Associate a cost with each performance metric • Monetary cost, path length cost and path diversitycost • Total cost of a selection of ISPs C: ct(C): = αmcm(C) + αpcp(C) + αdcd(C) • Optimization problem: Find the set C* with the minimum total cost • Brute Force approach is feasible • E.g. For |I|=15 and K=4, there are 1365 combinations • Solution approach: Evaluate the cost of each selection and choose the set with the minimum cost Amogh Dhamdhere IEEE Infocom 2006

9. Monetary and Path Length Cost • Lower level optimization problem: Given a set of ISPs C, what is the minimum monetary and path length cost of routing egress flows ? • Find the mapping G* of items to bins that minimizes the cost of the assignment (Bin Packing) • Flows = items • ISPs = bins • To find: Least cost assignment of flows to ISPs • NP hard ! • Use First Fit Decreasing (FFD) heuristic • Generated mapping G* very close to optimal • Monetary and path length costs of C are then calculated using the mapping G* Amogh Dhamdhere IEEE Infocom 2006

10. Path Diversity Cost • A selection C gives K paths to each destination d • K-shared link to d: A link which is shared by all K paths to d • If a K-shared link fails, destination d is unreachable • Minimize the number of K-shared links • Should give best performance for single-link failures • Define metric k(d,C): number of K-shared links to d in selection C • Choose the selection with the minimum k(d,C) averaged over all destinations Amogh Dhamdhere IEEE Infocom 2006

11. Evaluation - Bin Packing • FFD heuristic used to find the minimum monetary and path length costs • Simulations • Need exhaustive search to identify optimal cost • Restrict network to 3 ISPs and 15 destinations • FFD heuristic finds a solution with high probability, when average load is below 60-70% • In high load conditions, the probability of finding a solution decreases • Cost ratio is close to 1, even at high load conditions • FFD heuristic is close to the optimal in terms of cost Amogh Dhamdhere IEEE Infocom 2006

12. Evaluation – Path Diversity • AS-level paths and traffic rates are input to simulator • 9 ISPs, 250 destinations • Given K, find the selection C* with the minimum path diversity cost • For each selection C, u(C) = total traffic lost due to the failure of each link in topology • Calculate Δu(C) = u(C) – u(C*) for each selection C Single link failures: C* is the optimal selection 2,3 link failures: C* is close to the optimal selection Amogh Dhamdhere IEEE Infocom 2006

13. Egress Path Selection • After Phase-I, S has K upstream ISPs • Problem: How to map outgoing traffic to the ISPs • M flows: KM mappings of flows to ISPs • Some mappings may cause congestion to flows ! • Flows can be congested at access links or further upstream • Objective: Find the loss-free mapping with the minimum cost • Challenges: • Upstream topology and capacities are unknown • Cannot know a priori whether a mapping will cause congestion • Iterative routing approaches required Amogh Dhamdhere IEEE Infocom 2006

14. Egress Path Selection • Step 1: Use the FFD heuristic to map flows to ISPs • Assume initially that the access links are bottlenecks • Access capacities are known • FFD heuristic for bin packing gives a cost close to the best possible cost • Some flows may be congested • Bottlenecks in upstream networks • Use as the starting point for the stochastic search Amogh Dhamdhere IEEE Infocom 2006

15. Egress Path Selection • Step 2: Use iterative stochastic search to find a loss-free solution • Stochastic search by simulated annealing • Iterative combinatorial optimization algorithm • Route traffic, measure congestion, decide next action • Action: Flows re-routed from one ISP to another • can accept moves which increase congestion • Accepting “bad” moves can help to escape local minima Amogh Dhamdhere IEEE Infocom 2006

16. Evaluation of Stochastic Search • Stochastic search involves iterative routing • Traffic has to be re-routed • Some traffic may be dropped due to congested links • Evaluation metrics • Probability of finding a solution (high) • Number of iterations to find a solution (low) • Amount of traffic re-routed (low) • Amount of dropped traffic (low) • Compare against other heuristics • Only bin packing (access-link) • Greedy iterative algorithm (greedy-single) • Moves which increase congestion are not accepted • Variants of Simulated Annealing Amogh Dhamdhere IEEE Infocom 2006

17. Evaluation of Stochastic Search • SA-slow and greedy show similar probability of finding a solution • Other algorithms have a significantly lower probability of finding a solution • On the average, SA-slow needs fewer iterations to find a feasible solution • Accepting “worse” solutions can actually help find a loss-free solution faster • SA drops less traffic on the average than greedy-single • SA re-routes less traffic on the average than greedy-single Amogh Dhamdhere IEEE Infocom 2006

18. Summary of Contributions • Proposed practical algorithms for ISP selection and egress traffic allocation among selected ISPs • ISP selection algorithm takes into account both monetary and performance constraints • Formulated as a bin-packing problem • Applicable for general pricing functions • Can be extended to incorporate more performance metrics • Egress path selection without knowledge of upstream topology • Proposed simulated annealing for stochastic search • Performs better than other simple iterative algorithms Amogh Dhamdhere IEEE Infocom 2006

19. Thank You ! Amogh Dhamdhere IEEE Infocom 2006

20. Evaluation of Bin Packing • Simulations • Need exhaustive search to identify optimal cost • Restrict network to 3 ISPs and 15 destinations • FFD-like heuristic finds a solution with high probability, when average load is below 60-70% • In high load conditions, the probability of finding a solution decreases • Cost ratio is very close to 1, even at high load conditions • Heuristic algorithm is close to the optimal in terms of cost Amogh Dhamdhere IEEE Infocom 2006

21. Stochastic Search – Probability of finding a solution • bneck_loc=0.5 (bottlenecks in the middle of network) and bneck_shar=0 (no shared bottlenecks) • SA-slow and greedy-single show similar probability of finding a solution • Access-link, greedy-mult and SA-fast show significantly lower probability of finding a solution • Henceforth, compare only greedy-single and SA-slow Amogh Dhamdhere IEEE Infocom 2006

22. Stochastic Search – Number of Iterations • How many iterations before solution is found ? • Number of iterations required increases with offered load (average flow rate) • SA-slow performs better than greedy-single on the average • Accepting solutions with increasing congestion can actually help find a non-congested solution quicker Amogh Dhamdhere IEEE Infocom 2006

23. Stochastic Search – Results • What is the total rate of traffic that is re-routed ? • What is the total rate of traffic that is dropped due to congestion ? • SA-slow drops less traffic on the average than greedy-single • SA-slow re-routes less traffic on the average than greedy-single Amogh Dhamdhere IEEE Infocom 2006

24. Assumptions • Provision the egress traffic of S • The set of M major destinations is known • Average rates to the major destinations are known • Number of ISPs to choose (K) and the set of possible ISPs (I) is known • An ISP charges based on the volume of traffic routed through it (volume based charging) • Assume increasing and concave pricing functions Amogh Dhamdhere IEEE Infocom 2006