Optimizing Cost and Performance for Multihoming

1. Optimizing Cost and Performance for Multihoming Nick FeamsterCS 6250Fall 2011

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

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. 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. 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 = C0 + C(x) • C0: 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)

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

7. 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!

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

9. 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-qk) quantile of original traffic, where qk is ISP k’s charging percentile

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

11. 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

12. Algorithm Sketch • Determine charging volume for each ISP • Compute V0 • Find pk that minimize ∑k ck(pk) subject to ∑kpk=V0 using dynamic programming • Assign traffic given charging volumes • Non-peak assignment: ISP k is assigned  pk • Peak assignment: • First let every ISP k serve its charging volume pk • Dump all the remaining traffic to an ISP k that has bursted for fewer than (1-qk)*N intervals

13. 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

14. 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

15. Evaluation Method • 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

16. Conclusions • First paper on jointly optimizing cost and performance for multihoming • Propose novel smart routing algorithms that achieve both low cost and good performance • Under traffic equilibria, smart routing improves performance without hurting other traffic