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Joint Optimization of Scheduling and Congestion Control in Communication Networks

Joint Optimization of Scheduling and Congestion Control in Communication Networks. Matthew Andrews Bell Labs. Congestion control and scheduling. Inj rate x2. S2. Scheduling: Decide which data should be served at each internal queue Congestion control

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Joint Optimization of Scheduling and Congestion Control in Communication Networks

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  1. Joint Optimization of Scheduling and Congestion Control in Communication Networks Matthew Andrews Bell Labs

  2. Congestion control and scheduling Inj rate x2 S2 • Scheduling: • Decide which data should be served at each internal queue • Congestion control • Decide how much data should be injected into each flow • i.e. choose x1, x2, x3 S1 Inj rate x1 Inj rate x3 S3 t2 t3 t1

  3. Wireline networks Inj rate x2 • In wireline networks, scheduling and congestion control are typically only loosely coupled • Scheduling: • Simple FIFO is often used • No dependence on flow rates • Congestion control • TCP only uses coarse info from scheduler, e.g. RTTs, packet losses • TCP operates without knowledge of exact scheduling algorithm used S2 S1 Inj rate x1 Inj rate x3 S3 t2 t3 t1

  4. Wireless networks • This talk: • Three benefits to a tighter coupling of scheduling and congestion control in wireless networks • Pointer to algorithm that achieves this coupling

  5. Congestion Control • Does it make sense to talk about alternatives to TCP? • In wireless networks… maybe… • 3G networks employ proprietary congestion control over the wireless link • (e.g. www.venturiwireless.com) • Non-TCP based congestion control could be used in a self-contained ad-hoc network

  6. Congestion Control • One well known issue with TCP that I won’t discuss… • In wireless networks, TCP interprets losses due to interference as congestion and backs off too much • Well-studied issue • Well-known solutions • Snoop protocol ( Balakrishnan, Seshan, Amir, Katz) • Loss prevention (FEC, HARQ, DARQ, link-level retransmissions etc.)

  7. Joint Congestion Control + Scheduling • Three benefits of joint congestion control + scheduling in wireless networks • 1. Enables effective buffer sizing. Prevents excessive queue buildups. • 2. Prevents conflicts between rate allocation due to congestion control and rate allocation due to link scheduler • 3. Allows for network utility maximization, even in presence of complex queueing dynamics • (Applies to wireline as well as wireless networks)

  8. 1. Buffer sizing • Standard rule for buffer sizing in wireline networks • Buffer size = bandwidth delay product (BDP) • Enables full utilization of link rate • In wireless networks • BDP ill defined due to variable link bandwidth • Buffer sizing difficult • Buffer too small – don’t utilize bandwidth when link rate is high • Buffer too large – excessive delays when link rate is low link rate C(t) end2end prop delay d

  9. 1. Buffer sizing example • Two link rates, 1Mbps and 20kbps • 35KB buffer = 1Mbps x 280ms = 20kbps x 14s • Downlink packet latencies for download of CNN homepage 1s 10s 20kbps 1Mbps

  10. 1. Buffer sizing • Another example • Chakravorty and Pratt (2002) observed queues >30s in GPRS networks • Leads to unacceptable delays for other flows sharing same buffer • What would be better? • Have large buffers to deal with high bandwidth case • Recognize when link has low bandwidth • Make sure congestion control does not send too much data in this case

  11. 2. Scheduling vs congestion control ri (t) rj (t) service rate vector (r1(t),…,rn (t)) t • Opportunistic scheduling • At each time step, pick one user to serve • If user i chosen, serve at rate ri (t) • Ri (t) = ave service rate to user i

  12. 2. Scheduling vs congestion control ri (t) rj (t) service rate vector (r1(t),…,rn (t)) t • Proportional Fair wireless scheduling algorithm • At each time step, serve user argmax ri (t) / Ri (t) • Aim is to maximize i log Ri (t)

  13. 2. Scheduling vs congestion control • Conflict between scheduling and cong. control!! • Link scheduler tries to do optimal rate allocation among flows • E.g. Prop Fair aims to maximize i log Ri • TCP also tries to do rate allocation among flows • Whenever RTTs are low, TCP increases sending rate to try and get more bandwidth • But TCP can’t get more bandwidth than wireless link scheduler allows • All we get are bigger queues R3 R1 R2 Rates provided by Prop Fair

  14. 2. Scheduling vs congestion control • Better solution • Scheduling and congestion control work jointly to find optimal rate allocation

  15. 3. Network Utility Maximization • Wireline networks max Ui(xi) subject to . Q i xi  cQ • xi = inj rate into path i • cQ = capacity of server Q • Ui(.) = utility function (e.g. log) • Kelly-Maulloo-Tan, Low-Lapsley, Low-Peterson-Wang… • “TCP is a primal dual algorithm for solving this problem” S2 S3 S1 t1 server A server B t2 t3

  16. Example • Kelly, Maulloo, Tan • xi (t) = injection rate into flow i at time t • pQ = price for congestion at router Q

  17. 3. Network Utility Maximization • Wireless networks max Ui(xi) subject to . ( …, xi , … ) C • C = system capacity region (convex) • Depends on power assignments, interference etc. • Chiang • Joint power control and congestion control for solving problem

  18. 3: Convergence vs oscillations Inj rate x2 Inj rate x3 S2 S3 Inj rate x1 Server B S1 t1 Server A Cong at server A = x1 + x2 t2 t3 • Prior work: • Congestion of server Q = F(injection rates of flows that use Q) • For example, congestion is signaled before queues buildup (e.g. via ECN) • Arrival rate of flow at server = flow injection rate • Allows for convergence results

  19. 3: Convergence vs oscillations S2 S1 S3 t2 t3 • A-Slivkins (Infocom 06) • Suppose congestion causes queue buildups • Flow arrival rate at server not necessarily equal to flow injection rate • Do convergence results still hold? t1

  20. t1 t2 t3 3: Sending rates vs arrival rates inji(t) • due to queues that are upstream,in general arrival rates arr1(t), arr2(t), arr3(t)are different from sending rates inj1(t), inj2(t), inj3(t) • Suppose we use the true arrival rates • i.e. cong =arr1(t) + arr2(t) + arr3(t) S1 S2 S3 arri(t) server

  21. 3: Convergence vs oscillations • Result: for any TCP-like congestion control mechanism,there exists a network of (wireline) servers and a set of flows such that starting from a certain initial state, system oscillates in a suboptimal state at all times, for each server, the total sending rate of all flows that use this server is less than its capacity

  22. Joint scheduling + cong control xi xj • How should we do joint scheduling and congestion control? • Problem definition • Model queues explicitly • When data served by one server, passes to next server on flow route • Design scheduling + cong control max Ui(xi) subject to . All queues are stable

  23. Joint scheduling + cong control xi xj • Greedy Primal Dual Algorithm (Stolyar) • Simple version • Flow Routes are given • Set of routes to destination d form rooted tree • At most 1 pckt into each flow per time step • Per destination queues • Qv,d = amount of dest d data at node i • nv,d = next hop for Qv,d data • rv,w(t) = data rate between nodes v,w

  24. Joint scheduling + cong control xi xj • Greedy Primal Dual Algorithm (Stolyar) • Scheduling • Node v serves data from queue that maximizes (Qv,d – Qnv,d,d) rv,nv,d(t) • Scheduling • Flow i injects a packet whenever U’(xi(t)) – b.Qibegin < 0 b = small parameter Qibegin = first queue on path of flow i

  25. Joint scheduling + cong control xi xj • Greedy Primal Dual Algorithm (Stolyar) • Solves the utility maximization problem max Ui(xi) subject to . All queues are stable • Works for wireless networks • Joint scheduling and cong control • Collaboration via queues sizes • Do not get excessive queue buildup • Scheduling creates “backpressure”

  26. Conclusions • Three reasons why joint scheduling and congestion control make sense in wireless networks • Sketched one possible method for doing this (Stolyar) • Related work due to Eryilmaz-Srikant and Neely-Modiano-Li

  27. Conclusions • Implementation issues? • Need communication between scheduler and cong control • E.g. in Greedy Primal Dual algorithm, need to exchange queue sizes between neighbors • For wireless networks, need to exchange channel state information for optimal throughput • Could piggyback queue state info on channel state info • Different channel models? • Optimality of Greedy Primal Dual proved for stationary channels • What about adversarial channels?

  28. Backup slides • Oscillating example of A-Slivkins 2006.

  29. ?? • no congestion: all queues are empty S S t t • congestion: at least one queue is non-empty Parameterized congestion control • sender detects congestion using feedback from receiver • yes decrease sending rate; noincrease sending rate

  30. start Q1 Q1 Q1 Q2 Q2 Q2 Q3 Q3 Q3 Q4 Q4 Q4 S S S t t t 1 finish 1 The basic gadget: high-level idea capacity 1 <1 <1 1

  31. init rate  S’ capacity 1 <1 <1 1 init rate 0 1 t’ The basic gadget: animation Q1 Q2 Q3 Q4 S t

  32. capacity 1 <1 <1 1 Q1 Q2 Q3 Q4 S t The basic gadget: animation init rate  S’ rate = 0 t’

  33. capacity 1 <1 <1 1 Q1 Q2 Q3 Q4 S t rate The basic gadget: animation S’ rate = 0 t’

  34. capacity 1 <1 <1 1 Q1 Q2 Q3 Q4 S t The basic gadget: animation rate = 0 S’ rate = 0 1 t’

  35. rate capacity 1 <1 <1 1 Q1 Q2 Q3 Q4 S t The basic gadget: animation S’ rate = 0 t’

  36. before 1 1 2 2 3 3 4 4 • after 1 2 3 1 2 3 4 Connect two gadgets identify Q4 from one gadget with Q1 from another

  37. 1 2 3 1 2 3 1 2 3 1 2 3 Row of gadgets S1 t1 fluid eventually reaches its destination, and the process stopsnot too exciting!

  38. 1 2 3 1 2 3 1 2 3 1 2 3 Row of gadgets S1 t1 a better process

  39. refill 1 2 3 1 2 3 1 2 3 1 2 3 Row of gadgets and now a refill happens S1 t1 a better process

  40. refill 1 2 3 1 2 3 1 2 3 1 2 3 Row of gadgets ... and so on S1 t1 a better process

  41. S1 t1 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 S2 t2 S3 t3 Rows of gadgets each row is running the same process, shifted in time

  42. refill S1 t1 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 refill S2 t2 refill S3 t3 Rows of gadgets now a refill happens each row is running the same process, shifted in time

  43. refill S1 t1 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 S2 t2 S3 t3 Rows of gadgets ... and so on Wait a sec... how do the refills happen ???

  44. S1 t1 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 S2 t2 S3 t3 S4 t4 Refills and vertical sessions rate >0

  45. S1 t1 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 S2 t2 S3 t3 S4 t4 Refills and vertical sessions rate >0

  46. S1 t1 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 S2 t2 S3 t3 S4 t4 Refills and vertical sessions rate >0

  47. S1 t1 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 S2 t2 S3 t3 rate S4 t4 Refills and vertical sessions

  48. S1 t1 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 1 1 1 1 2 2 2 2 3 3 3 3 S2 t2 S3 t3 S4 t4 Refills and vertical sessions refilled !!! rate = 0

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