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A Simple QoS Packet Scheduler for Network Routers

A Simple QoS Packet Scheduler for Network Routers. Su Wen Presenting the paper in SIGCOM 2001 “SRR: An O(1) Time Complexity Packet Scheduler for Flows in Multi-Service Packet Networks”. Quality of Service. Current Internet Service Best effort Packet scheduling: FIFO QoS

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A Simple QoS Packet Scheduler for Network Routers

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  1. A Simple QoS Packet Scheduler for Network Routers Su Wen Presenting the paper in SIGCOM 2001 “SRR: An O(1) Time Complexity Packet Scheduler for Flows in Multi-Service Packet Networks”

  2. Quality of Service • Current Internet Service • Best effort • Packet scheduling: FIFO • QoS • Demanded by new network services • multimedia, audio/video • Meets requirement in bandwidth, delay, jitter

  3. QoS Approaches • Integrated Services • Provide service guarantees to individual flows in the network • GPS - Generalized Processor Sharing • Weighted Fair Queuing • Differentiated Services • Provide some service guarantees to different classes of traffic in the network • Example: hierarchical link-sharing • audio, video, telnet, ftp, mail

  4. server r . . . GPS Scheduler Bandwidth Sharing • GPS - generalized processor sharing • ideal model: each flow gets its fair share of BW • Si(t1, t2) / Sj(t1, t2) = wi/wj • ri/r = wi/jwj flows • wi - weight of flow i • Si (t1, t2) - amount of traffic served for flow I during time t1 to t2 • ri - service rate of flow i • r - service rate of the server

  5. Bandwidth Sharing Implementations • Weighted Fair Sharing • An approximation/implementation of GPS • Each flow has separate queues • Two approaches: • time-stamp based: e.g. virtual clock • round robin

  6. Implementation Characteristics • Time Stamp based: • Fairly good fairness and delay bound • Time complexity: O(logN) - O(N) • Round Robin based: DRR, CORR • Time complexity: O(1) • Short term unfairness • bursty output

  7. SRR - Smoothed Round Robin • Good short term and long term fairness • emulate GPS • work conserving • forwards packets as long as there are active flows • O(1) time complexity • one lookup to decide what to send • Better scalability • Simplicity

  8. Data Structures • Weight Matrix (WM) • number of rows = N - number of flows • number of columns = k = log2wmax + 1 • # of binary digits needed to represent the max normalized weight • WM size depends on # of flows and BW granularity • WSS - Weight Spread Sequence • WSS of order k (Sk) is a sequence of integers from 1 to k • recursively defined • S1 = 1 • Sk = Sk-1, k, Sk-1

  9. Weight Matrix • Four flow f1 - f4, each with different BW requirement: • r1 = 64kbps, r2 = 128kbps, r3 = 320kbps, r4 = 192kbps • w1 = 1 -> {0 0 1} • w2 = 2 -> {0 1 0} • w3 = 5 -> {1 0 1} • w4 = 3 -> {0 1 1} • col1 col2 col3 • row1 0 0 1 • row2 0 1 0 • row3 1 0 1 • row4 0 1 1

  10. Weight Spread Sequence Rule: S1 = 1 Sk = Sk-1, k, Sk-1 • To find WSS of order 3 - S3 • S2, 3, S2 • S1, 2, S1, 3, S1, 2, S1 • 1, 2, 1, 3, 1, 2, 1 N - number of flows wi - normalized weight of flow i k - log2wmax + 1

  11. deficiti - bytes need to be served of flow i Li - length of packet of flow i Lmax - length of maximum packet size Serve_flow(): deficitj += Lmax while (Lj <= deficitj) { send pktj deficitj -= Lj } Scheduling Algorithm • Generate a Service Queue: • for each value i in the WSS • if jth row of column i of WM has value 1 • add flow j to the queue • Serve the flow indicated in the service queue in a round robin fashion • f1: {0 0 1} • f2: {0 1 0} • f3: {1 0 1} • f4: {0 1 1} S3: 1, 2, 1, 3, 1, 2, 1 Flow Service Queue: f2, f4, f3 f2, f4, f3, f1, f3, f4, f3 f3,

  12. Fairness • Long Term • Si(0, t) / Sj(0, t) = wi/wj • Si(0, t)/wi - Sj(0, t)/wj = 0 • Short Term • Si(0, t)/wi - Sj(0, t)/wj < (k+2)Lmax/2*min(wi,wj) Si(t1, t2) - amount of traffic served for flow i during time t1 to t2 wi - normalized weight of flow i k - log2wmax + 1, the order of WSS

  13. Delay • Df - maximum delay for flow f • Df < 2*Lmax/wf + 2*(N-1)*Lmax/ iwi • Inverse proportional to the weight of the flow • Proportional to total number of flows • Not strictly rate proportional, but better than DRR • simulation result shows so

  14. Scalability • Space requirement • N x k • At 1kbps granularity, k=16 can accommodate rates up to 64Mbps • At 1Mbps granularity, k=16 can accommodate rates up to 64Gbps • Time complexity • O(1) to choose packets for transmission • O(k) to add or delete a flow

  15. SRR Problems • add_flow() and delete_flow() operations • SRR scheduler is called during busy period • packets are back-logged • add_flow() is called when new flow arrives • delete_flow() is called when queue of the flow is empty • If a flow is not back logged, add_flow() and may be called for every packet • quite expensive: O(k) • Solution: • delay the deletion of a flow • not strictly O(1) any more

  16. Conclusion • SRR is simple • even I can understand! • Scalable • time and space • Good performance • fairness • delay

  17. Questions? • How did he come up with the algorithm?

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