Combining multihoming with overlay routing (or, how to be a better ISP without owning a network)

1. Combining multihoming with overlay routing(or, how to be a better ISP without owning a network) Yong Zhu, Constantine Dovrolis, and Mostafa Ammar Georgia Institute of Technology Speaker: Chen-Hung Yu

2. Basic form of Internet • Singlehoming

3. Multihoming & Overlay routing

4. OSP – Overlay Service Provider • Operates a Multihomed Overlay Network (MON) • MON node – a multihomed router • An Internet provider that does not own a network

5. MON nodes • If a MON node is multihomed with K ISPs  each flow has K direct MON paths • With N MON nodes  each flow increases to indirect MON paths

6. Outline • Introduction • Model and Problem Formulation • MON design heuristics • Evaluation and Discussions • Conclusions

7. MON design problem • Where to place MON nodes and how to select upstream ISPs for each nodes. • Objectives • Profitable • Better performance • Less expensive • Revenue, cost, customer subscribe

8. The problem involves … • ISPs • Performance of the native network • The location and traffic matrix of potential customers • The OSP routing strategy • Pricing function • Node deployment costs

9. ISPs and the native network • POP p = (l, i) – the access point to ISP i at location l, P denotes the set of all POPs • LOC(p) = l; ISP(p) = i • We denote Il as the set of all ISPs the can be connected from location l • Native-layer performance – matrix • The entry represents the propagation RTT from POP p to q

10. Estimate the matrix T • Directly measure – e.g., ping • If can’t, try to find the model • Mostly depends on the physical distance between two POPs • Intradomain – “highway driving distance” • Interdomain – RTT increases with the number of AS in the route

11. Intradomain cases Driving distance from p to q

12. Interdomain cases A constant depends on the # of AS hops h

13. MON representation • A MON node is present at POP p if the node is located at LOC(p) and connected to ISP(p) • POP selection vector • The locations of all MON nodes

14. Customers and OSP-preferred flows • The workload of customer u is a set of flows F(u) • A flow f = (sf, df, rf, τf) • OSP-preferred flow and OSP-preferred path • Subscribe – At least a fraction H of a customer’s traffic is in OSP-preferred flows

15. OSP routing strategy • Direct-Routing-First (DRF)

16. OSP revenues • Let be the OSP pricing function • The total OSP revenue • Required upstream capacity at POP p • Total capacity cost The pricing function used by the ISP at POP p

17. OSP costs • Required upstream capacity at POP p • Total capacity cost • Total node deployment cost The pricing function used by the ISP at POP p Cost of deploying a MON node at location l

18. Problem statement • Inputs: • Native network information • OSP information • Customer information • Determine the POP selection vector MON to maximize the profit:

19. Problem statement (cont.) • constraints • At most N MON nodes • Maximum multihoming degree • NP-hardness • Reduction from the set covering problem

20. MON design heuristics • Two major tasks: • Select up to N locations for placing MON nodes • Select up to K upstream ISPs for each deployed MON node • Present four heuristics differ in terms of their inputs

21. Four Heuristics • RAND • CUST – • places N MON nodes at the locations with the maximum number of customers • Each selected l then selects the locally present ISPs with the maximum coverage • TRFC – uses the aggregated traffic rate that originates from all potential customers • Places MON nodes at locations where “traffic heavy” customers are located • Select ISPs that receive the maximum traffic rate from customers • PERF

22. Performance-driven (PERF) • If there are OSP-preferred direct paths, then CUST and TRFC perform quite well. • However, when many customer flows only have indirect OSP-preferred paths, they will fail. • Associate

23. Evaluation • Compare the MON design heuristics • OSP profitability and performance • Depending on # of MON nodes, degree of multihoming, node deployment cost, OSP/ISP pricing ratio • Examine various OSP routing strategies

24. Simulation setup • Customer • CUST-POPUL, CUST-UNFRM • Flow • RATE-GRVTY, RATE-UNFRM • Pricing

26. Effect of number of MON nodes

27. Effect of OSP routing strategy

28. Effect of pricing ratio

29. Effect of maximum multihoming degree

30. conclusions • Examine multihoming + overlay routing from the pragmatic perspective of an OSP • Meet the objectives – profitable、better performance、less expensive • Use a performance-aware MON design heuristic • Deploy nodes at “key” locations • Connect each MON node to ISPs that can directly reach traffic-heavy destination POPs • Direct path > indirect path • Charge less than competing native ISPs • Determine good trade-off between the # of MON nodes and multihoming degree  based on the d(l)