1. Optimizing Cost and Performance for Multihoming ACM SIGCOMM 2004 Lili Qiu Microsoft Research liliq@microsoft.com Joint Work with D. K. Goldenberg, H. Xie, Y. R. Yang, Yale University Y. Zhang, AT&T Labs – Research

2. 2 Multihoming & Smart Routing Multihoming –A popular way of connecting to Internet Smart routing –Intelligently distribute traffic among multiple external links User ISP 1 ISP K Internet ISP 2

3. 3 Potential Benefits Improve performance –Potential improvement: 25+% [Akella03] –Similar to overlay routing [Akella04] Improve reliability –Two orders of magnitude improvement in fault tolerance of end-to-end paths [Akella04] Reduce cost Q: How to realize the potential benefits?

4. 4 Our Goals Goal –Design effective smart routing algorithms to realize the potential benefits of multihoming Questions –How to assign traffic to multiple ISPs to optimize cost? –How to assign traffic to multiple ISPs to optimize both cost and performance? –What are the global effects of smart routing?

5. 5 Related Work Techniques for implementing multihoming –BGP peering, DNS-based, NAT-based (e.g., [RFC2260, Cisco, GCLC04, Radware, F5]) –Complementary to our work Performance evaluation [Akella03,Akella04] –Quantify the potential benefits of multihoming –Unaddressed challenge: how to achieve this in practice Smart routing –Commercial products (e.g., [RouteScience, Internap, Proficient, …]) –Technical details are unavailable Hash-based load balancing [Cao01, Guo04] –Optimizes neither performance nor cost

6. 6 Network Model Network performance metric –Latency (also an indicator for reliability) –Extend to alternative metrics log (1/(1-lossRate)), or latency+w*log(1/(1-lossRate)) ISP charging models –Cost = C 0 + C(x) –C 0 : a fixed subscription cost –C : a piece-wise linear non-decreasing function mapping x to cost –x : charging volume Total volume based charging Percentile-based charging (95-th percentile)

7. 7 Percentile Based Charging Interval Sorted volume N 95%*N Charging volume: traffic in the (95%*N)-th sorted interval

8. 8 Why cost optimization? A simple example: –A user subscribes to 4 ISPs, whose latency is uniformly distributed –In every interval, the user generates one unit of traffic To optimize performance –ISP 1: 1, 0, 0, 0, … –ISP 2: 0, 1, 0, 0, … –ISP 3: 0, 0, 1, 0, … –ISP 4: 0, 0, 0, 1, … –95th-percentile = 1 for all 4 ISPs –95th-percentile = 1 using one ISP Cost(4 ISPs) = 4 * cost(1 ISP) Optimizing performance alone could result in high cost!

9. 9 Cost Optimization: Problem Specification (2 ISPs) Time Volume N12

10. 10 Cost Optimization: Problem Specification (2 ISPs) Time Volume P1 P2 Goal: minimize total cost = C1(P1)+C2(P2) Sorted volume

11. 11 Issues & Insights Challenge: traditional optimization techniques do not work with percentiles Key: determine each ISP’s charging volume Results –Let V0 denote the sum of all ISPs’ charging volume –Theorem 1: Minimize cost  Minimize V0 –Theorem 2: V0 ≥ 1-  k=1..N (1-q k ) quantile of original traffic, where q k is ISP k’s charging percentile

12. 12 Cost Optimization: Problem Specification (2 ISPs) Time Volume P1 P2 P1 + P2  90-th percentile of original traffic Sorted volume

13. 13 Intuition for 2-ISP Case ISP 1 has  5% intervals whose traffic exceeds P1 ISP 2 has  5% intervals whose traffic exceeds P2 The original traffic (ISP 1 + ISP 2 traffic) has  10% intervals whose traffic exceeds P1+P2 P1+P2  90-th percentile of original traffic

14. 14 Sketch of Our Algorithm 1.Determine charging volume for each ISP –Compute V0 –Find p k that minimize ∑ k c k (p k ) subject to ∑ k p k =V0 using dynamic programming 2.Assign traffic given charging volumes –Non-peak assignment: ISP k is assigned  p k –Peak assignment: First let every ISP k serve its charging volume p k Dump all the remaining traffic to an ISP k that has bursted for fewer than (1-q k )*N intervals

15. 15 Additional Issues Deal with capacity constraints Perform integral assignment –Similar to bin packing (greedy heuristic) Make it online –Traffic prediction Exponential weighted moving average (EWMA) –Accommodate prediction errors Update V0 conservatively Add margins when computing charging volumes

16. 16 Optimizing Cost + Performance One possible approach: design a metric that is a weighted sum of cost and performance –How to determine relative weights? Our approach: optimize performance under cost constraints –Use cost optimization to derive upper bounds of traffic that can be assigned to each ISP –Assign traffic to optimize performance subject to the upper bounds

17. 17 Evaluation Methodology Traffic traces (Oct. 2003 – Jan. 2004) –Abilene traces (NetFlow data on Internet2) RedHat, NASA/GSFC, NOAA Silver Springs Lab, NSF, National Library of Medicine Univ. of Wisconsin, Univ. of Oregon, UCLA, MIT –MSNBC Web access logs Realistic cost functions [Feb. 2002 Blind RFP] Delay traces –NLANR traces: 3 months’ RTT measurements between pairs of 140 universities –Map delay traces to hosts in traffic traces

18. 18 Baseline Algorithms 1.Round robin –In each interval, assign traffic to a single ISP –Rotate in a round robin fashion 2.Equal split –In each interval, split traffic equally among ISPs –Similar to hash-based load balancing 3.Offline local fractional –Minimize the total cost for each interval independently 4.Dedicated links –Flat rate and independent of usage

19. 19 Cost Comparison for Different Traces Our algorithms significantly out-perform the alternatives.

20. 20 Cost Comparison for Varying # Links For all # ISPs, our cost optimization performs well.

21. 21 Cost + Performance Evaluation Optimizing performance alone often doubles the cost.

22. 22 Cost + Performance Evaluation (Cont.) Our dual metric optimization achieves low cost and latency.

23. 23 Global Effects of Smart Routing Selfish nature of smart routing –Each user optimizes its own cost & performance without considering its impact on other traffic –Need to understand its global effects Questions –How well does smart routing perform when traffic assignment affects link latency? –How well do different smart routing users co-exist? –How well do smart routing users co-exist with single- homed users?

24. 24 Evaluation Methodology Abilene traffic traces Rocketfuel inter-domain topology –170 nodes, 600 edges –With propagation delay and OSPF weights –M/M/1 queuing model Routing –A user selects best performing ISP subject to cost constraints –Inter-domain: shortest AS hop count –Intra-domain: OSPF Compute traffic equilibria as in [QYZS03]

25. 25 Global Effects: Summary Impact of self interference is small Smart routing users co-exist well with each other Smart routing users co-exist well with single-homed users

26. 26 Conclusions Contributions –First paper on jointly optimizing cost and performance for multihoming –Propose a series of novel smart routing algorithms that achieve both low cost and good performance –Under traffic equilibria, smart routing improves performance without hurting other traffic Future work –Further evaluation through Internet experiments –Dynamics of interactions among different users –Design better charging models

27. 27 Thank you!