1 / 37

Design Issues of Reserved Delivery Subnetworks

Design Issues of Reserved Delivery Subnetworks. Ruibiao Qiu Department of Computer Science and Engineering Washington University in St. Louis April 28, 200 5. Motivations. Lack of bandwidth reservation in today’s Internet Dominant best-effort traffic Why per-flow reservation not deployed?

willis
Download Presentation

Design Issues of Reserved Delivery Subnetworks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Motivations • Lack of bandwidth reservation in today’s Internet • Dominant best-effort traffic • Why per-flow reservation not deployed? • Cost of equipment, management and operation • Support in applications and end hosts

  3. 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. 1.14 2.2 Reserved Delivery Subnetwork (RDS) • A mechanism to provide better service for large aggregates of users • Larger aggregate is more efficient Overload probability 100 flows 10,000 flows aggregate flow bursty flows (peak/avg=25) Per-flow reservation / average

  5. 70Mb/s 70Mb/s 120Mb/s 15Mb/s 10Mb/s 10Mb/s An RDS Example

  6. Example RDS Sink node Other node Source node

  7. Outline • Introduction • Configuration of RDSs with single server • Source traffic regulation in an RDS • Summary

  8. 2 2 2 4 8 7 7 7 reservation Single-server RDS Configuration • Bandwidth reservation for an RDS • Satisfy all user demands • Use bandwidth efficiently • Formulated as a graph problem 1 5,1 5,3 3,2 2,2 4,3 2,4 2 9,4 s 9,1 8,3 7,2 5,2 4,4 7,2 8,5 5 sink demand capacity,length

  9. 1,2 1,2 1,2 1,2 1 1,0 1 2 2,4 7,8 1,2 2 2,0 2 8,9 8,9 5 2 5,7 2 5 5,7 5,7 flow 5,8 4 5,0 8 flow flow,reservation flow=8,total cost=101 flow=8,total cost=75 flow,reservation 7 7 7 reservation t 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 sink demand 1 5,1 5,3 3,2 2,2 4,3 2,4 2 9,4 s 9,1 8,3 7,2 5,2 4,4 7,2 8,5 5 capacity,length

  10. C(f ) reservation f 0 Link Cost Function • Bandwidth economy of aggregation • Larger flow aggregate, smaller fraction of traffic variation • Individual flows as i.i.d. random variables {X1, X2, …, Xn}, and aggregate flow as X = Xi •  = i,  = (i2)1/2 • A concave function • Reservation grows more slowly than flow size • Concave link cost function C(f ) = l  (f + kf) link length

  11. 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. t unit cost Least Cost Augmentation (LCA) • Optimal solutions in linear costs networks • Saturate path with least incremental cost 2 1 1 2 1 1 1 5 1 s 1 2 2 1 1 1 1 1

  13. 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 Flow increment = 1 Flow increment = 10 50 6 10 100 2 8 15 70 10 1 60 9 4 20 100 10 incremental cost 12 80

  14. 4 10 2 5 t 4 10 2 5 2 5 25 10 5 2 2 5 10 4 4 10 2 5 2 5 incremental cost incremental cost 5 2 2 5 Largest Demand First (LDF) • Approximate LCAin concave costs networks • Largest sink demand as flow increment 1 0 0 5 s 0 2

  15. s Evaluation of LDF • Simulations topologies • Torus network • National network (50 metro areas) • Random source and ≤50 sinks • Variables • Number of sinks • Flow variations

  16. 4 3 2 5 1 2 3 4 5 6 7 1 6 7 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

  17. 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. Performance Evaluation • Solutions evaluated • LDF • Largest demand first • EB(n) • Estimated lower bound with n “slices” • SPT • Shortest path tree as approximation • SPT(C) • Assuming no “incidental sharing” (star network) • Provide an upper bound • Measure relative cost to lower bound (EB(1))

  19. Simulation Results • LDF within a constant factor of lower bound

  20. Example RDS Sink node Other node Source node

  21. 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. 3 3 incremental cost +1.5 +1.5 2 2 1 -2 1 1 -2 1 -2 redirected flow Negative Cost Cycles • Undirectional cycles • Redirecting flow along the cycle Negative total incremental cost 3 3 flow After redirection, incremental cost = -3, lower cost solution

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

  24. 1 1 2 2 3 1 1 2 1 redirected flow Bicycles • Negative bicycles in concave cost graphs • Reduction leads to further cost improvements • New discovery 1000 current flow edge cost= l (f+f1/2) original cost: 8800 a b 1000 after redirection: 8700 1000 path distance

  25. Simulation Results • Limited improvements by local searches • Performance of LDF sufficient

  26. 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. Outline • Introduction • Configuration of RDS with a single server • Traffic regulation in an RDS • Summary

  28. 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. Unbalanced Bandwidth Utilization • Caused by overloaded sink • Overload some paths • Leave other paths under utilized • Avoidable in an RDS Actual usage Overloaded Sink Total reservation Source Unused Under utilized

  30. 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. Per-connection Regulation • Favor the less congested sinks sinks r1 source Bo(1) C1 Bi(1) R(1) r2 Bo(2) C1 R1 R(2) C2 Bi(2) R1 C2 r3 R R(3) Bo(3) Bi(3) R(4) C3 C3 Bi(4) r4 Bo(4) R(5) r5 Bi(5) Bo(5) Order: 1, 2, 3, 4, 5 Order: 2, 3, 4, 5, 1

  32. Aggregated Regulation • Per-connection traffic regulation overhead • Aggregated information for feedback control Bi(1) R(1) Bi(2) RDS R(2) Bi(3) R(3) source sink

  33. Simulations • Three sinks • 100 flows/sink • Avg. 1Mb/s per flow • After 10s, one sink has additional 700 flows 200Mb/s 400Mb/s 200Mb/s 300Mb/s 200Mb/s

  34. Simulation Results • Improve fairness, penalize overloaded sinks

  35. 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. 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. Questions?

More Related