1. Design Issues of Reserved Delivery Subnetworks Ruibiao Qiu Department of Computer Science and Engineering Washington University in St. Louis April 28, 2005

2. 2 - Ruibiao Qiu - 4/28/2005 Motivations Lack of bandwidth reservation in today’s Internet »Dominant best-effort traffic »Why per-flow reservation not deployed? l Cost of equipment, management and operation l Support in applications and end hosts

3. 3 - Ruibiao Qiu - 4/28/2005 Motivation (cont.) Aggregate bandwidth reservation: an alternative solution »Exclusive bandwidth reservation for aggregate of users »Manageable deployment for network providers »No change in applications and end hosts Numerous real world applications »Content delivery networks »Virtual private networks »Grid computing

4. 4 - Ruibiao Qiu - 4/28/2005 Reserved Delivery Subnetwork (RDS) A mechanism to provide better service for large aggregates of users Larger aggregate is more efficient 100 flows 10,000 flows 2.2 1.14 bursty flows (peak/avg=25) aggregate flow Per-flow reservation / average Overload probability

5. 5 - Ruibiao Qiu - 4/28/2005 An RDS Example 120Mb/s 70Mb/s 15Mb/s10Mb/s

6. 6 - Ruibiao Qiu - 4/28/2005 Example RDS Source node Sink nodeOther node

7. 7 - Ruibiao Qiu - 4/28/2005 Outline Introduction Configuration of RDSs with single server Source traffic regulation in an RDS Summary

8. 8 - Ruibiao Qiu - 4/28/2005 Single-server RDS Configuration Bandwidth reservation for an RDS »Satisfy all user demands »Use bandwidth efficiently Formulated as a graph problem s 5,35,3 4,34,3 5,15,1 9,19,1 9,49,4 3,23,2 4,44,4 7,27,2 7,27,2 5,25,2 8,58,5 8,38,3 1 2,42,4 2 5 capacity,length 2,22,2 sink demand 4 reservation 2 8 2 7 7 7 2

9. 9 - Ruibiao Qiu - 4/28/2005 Problem Formulation Transformation to a network flow problem »Flow: average aggregate traffic on a link »Link cost = reservation x length »An RDS corresponds a minimum cost maximum flow s 5,35,3 4,34,3 5,15,1 9,19,1 9,49,4 3,23,2 4,44,4 7,27,2 7,27,2 5,25,2 8,58,5 8,38,3 t 1,01,0 2,02,0 5,05,0 1 2,42,4 2 5 capacity,length 2,22,2 sink demand 4 reservation 2 8 2 7 7 7 2 2,42,4 flow 1 2 5 1,21,2 7,87,8 1,21,2 5,75,7 5,75,7 5,75,7 1,21,2 flow=8,total cost=101 flow,reservation flow=8,total cost=75 flow 1 2 5 1,21,2 5,85,8 8,98,98,98,9 1,21,2 flow,reservation

10. 10 - Ruibiao Qiu - 4/28/2005 Bandwidth economy of aggregation »Larger flow aggregate, smaller fraction of traffic variation »Individual flows as i.i.d. random variables {X 1, X 2, …, X n }, and aggregate flow as X =  X i l  =  i,  = (  i 2 ) 1/2 »A concave function l Reservation grows more slowly than flow size Concave link cost function C(  f ) = l  (  f + k  f ) Link Cost Function link length reservation C(  f ) ff 0

11. 11 - Ruibiao Qiu - 4/28/2005 Min-cost Max-flow Problem Find min. cost flow among all max. flows Efficient algorithms exist for linear cost networks For concave cost networks »NP-hard problems [Guisewite-Pardalos 1990] »Search-based exact algorithms impractical »Efficient approximation algorithms needed

12. 12 - Ruibiao Qiu - 4/28/2005 Least Cost Augmentation (LCA) s t 2 1 2 1 1 11 5 1 2 1 1 1 2 1 1 1 Optimal solutions in linear costs networks Saturate path with least incremental cost unit cost

13. 13 - Ruibiao Qiu - 4/28/2005 Challenge: Concave Link Cost Effects Incremental cost »Linear cost links: linear to flow increment »Concave cost links: depends on current flow & flow increment Same initial flow, different flow increments  different augmentation paths 2 6 8 4 9 12 15 50 70 20 60 80 incremental cost Flow increment = 1 Flow increment = 10 10 1 100 10

14. 14 - Ruibiao Qiu - 4/28/2005 Largest Demand First (LDF) Approximate LCA in concave costs networks Largest sink demand as flow increment s t 0 0 0 10 5 5 55 2525 5 5 5 5 5 incremental cost 1 5 2 4 2 4 2 22 10 4 2 2 2 4 2 2 incremental cost

15. 15 - Ruibiao Qiu - 4/28/2005 Evaluation of LDF Simulations topologies »Torus network »National network (50 metro areas) »Random source and ≤50 sinks Variables »Number of sinks »Flow variations s

16. 16 - Ruibiao Qiu - 4/28/2005 Maximum Sink Sharing Sort sinks by their distances to source Assume all sinks share a single (unrealistically) path to the source A loose bound s 4 3 1 5 7 6 2 1 2 34567

17. 17 - Ruibiao Qiu - 4/28/2005 Estimated Lower Bounds Equally partition nodes on a “disc” geographically Sort sinks by distance in each partition Assume all sinks share a single path to source s

18. 18 - Ruibiao Qiu - 4/28/2005 Performance Evaluation Solutions evaluated »LDF l Largest demand first »EB(n) l Estimated lower bound with n “slices” »SPT l Shortest path tree as approximation »SPT(C) l Assuming no “incidental sharing” (star network) l Provide an upper bound Measure relative cost to lower bound (EB(1))

19. 19 - Ruibiao Qiu - 4/28/2005 Simulation Results LDF within a constant factor of lower bound

20. 20 - Ruibiao Qiu - 4/28/2005 Example RDS Source node Sink nodeOther node

21. 21 - Ruibiao Qiu - 4/28/2005 A Local Search Approach Local search »Find the local optimal with efficient operations from a solution »An effective approximation method for combinatorial problems Using local search for local optimal solutions »Further improve solution quality »Measure the optimality of LDF solutions

22. 22 - Ruibiao Qiu - 4/28/2005 Negative Cost Cycles Undirectional cycles Redirecting flow along the cycle  Negative total incremental cost 3 3 2 2 1 1 1 3 3 After redirection, incremental cost = -3, lower cost solution flow +1.5 -2 incremental cost 1 redirected flow

23. 23 - Ruibiao Qiu - 4/28/2005 Cycle Reduction An efficient operation for local search Must work in concave cost links -2 -6 -4 v x w u non-flow edge 1 flow 3 5 w v x u s non-flow edge -2 -6 -4 3 7 6 v x w u flow 2 6 1

24. 24 - Ruibiao Qiu - 4/28/2005 Bicycles Negative bicycles in concave cost graphs Reduction leads to further cost improvements New discovery a b 1000 path distance 2 1 1 redirected flow 2 2 1 current flow 1 1 3 edge cost = l (f+f 1/2 ) original cost: 8800 after redirection: 8700

25. 25 - Ruibiao Qiu - 4/28/2005 Simulation Results Limited improvements by local searches Performance of LDF sufficient

26. 26 - Ruibiao Qiu - 4/28/2005 Contributions Study precise aggregate bandwidth reservation in an RDS Formulate the network design problem as a minimum cost network flow problem Introduce more realistic concave cost functions Propose an efficient approximation solution for the NP-Hard problem Develop local search improvements with cycle and bicycle reduction

27. 27 - Ruibiao Qiu - 4/28/2005 Outline Introduction Configuration of RDS with a single server Traffic regulation in an RDS Summary

28. 28 - Ruibiao Qiu - 4/28/2005 End-to-end Performance Potentials Performance limitation in ordinary Internet RDS makes end-to-end performance improvements possible »Informed data transfer [Savage et al 99] »Knowledge about underlying network »Information about the data backlog at both ends Example »Solving unbalanced bandwidth utilization problem

29. 29 - Ruibiao Qiu - 4/28/2005 Unbalanced Bandwidth Utilization Caused by overloaded sink »Overload some paths »Leave other paths under utilized Avoidable in an RDS Total reservation Actual usage Unused Source Sink Overloaded Under utilized

30. 30 - Ruibiao Qiu - 4/28/2005 Source Traffic Regulation Source schedules data transfers to maximize bandwidth utilization Data transfer scheduling algorithm »Estimate sink draining time »Order sinks by increasing order of draining time »Always allow the fastest draining sink to send with maximum allowed rate

31. 31 - Ruibiao Qiu - 4/28/2005 Per-connection Regulation Favor the less congested sinks sinks B i (1) R R1R1 C3C3 C1C1 C2C2 C3C3 C2C2 C1C1 B o (1) B i (5) B i (4) B i (3) B i (2) R(2) R(1) R1R1 R(3) R(4) R(5) B o (2) B o (3) B o (4) B o (5) r1r1 r2r2 r3r3 r4r4 r5r5 source Order: 1, 2, 3, 4, 5 Order: 2, 3, 4, 5, 1

32. 32 - Ruibiao Qiu - 4/28/2005 Aggregated Regulation Per-connection traffic regulation overhead Aggregated information for feedback control RDS B i (1) B i (3) B i (2) R(1) R(2) R(3) sink source

33. 33 - Ruibiao Qiu - 4/28/2005 Simulations Three sinks 100 flows/sink Avg. 1Mb/s per flow After 10s, one sink has additional 700 flows 400Mb/s 200Mb/s 300Mb/s 200Mb/s

34. 34 - Ruibiao Qiu - 4/28/2005 Simulation Results Improve fairness, penalize overloaded sinks

35. 35 - Ruibiao Qiu - 4/28/2005 Summary RDS: an effective alternative to per-flow reservation »Improved quality of service for aggregate of users »Easy to implement »Compatible with existing applications Research focus: RDS design issues »Configuration of single-server RDS »Configuration of multi-server RDS »Source traffic regulation inside RDS

36. 36 - Ruibiao Qiu - 4/28/2005 Previous Research Projects ALX (adaptation layer translator)-based studio quality video conferencing system over broad band WAN (ICME02,GLOBECOM02) Studies of Motion JPEG2000 and its applications in video processing and multimedia communications (EI02,EI03) Quality-scalable Motion-JPEG2000 video streaming over active networks (EI03) Cost-based routing in ad hoc wireless networks Contributions to ACE code base ATM stream interfaces on Windows and Solaris

37. 37 - Ruibiao Qiu - 4/28/2005 Questions?

38. 38 - Ruibiao Qiu - 4/28/2005 Bicycle Reduction Redirect flow on two cycles r v x w y Non-tree vertices f(q,y) p q f(p,x) u r v x w y f(q,y) p q f(u,p)-f(p,x) f(p,x) f(v,w)+f(q,y)+f(p,x) u f(u,q)-f(q,y t v u x w to tree vertices In-tree path cost s non-tree edge cost y

39. 39 - Ruibiao Qiu - 4/28/2005 An Extreme Bicycle Example No negative single cycle Improvement up to n s n n-1 n-2 m 1 link distance n-1 n n-2 m 1 1+ 

40. 40 - Ruibiao Qiu - 4/28/2005 Simulation Results Limited improvements by local searches Performance of LDF sufficient

41. 41 - Ruibiao Qiu - 4/28/2005 Simulation Results Improve fairness, penalize overloaded sinks