1 / 54

QoS Support in Edge Routers

PhD Thesis Defense. QoS Support in Edge Routers. Idris A. Rai Institut Eurecom/Telecom Paris France 15 th September 2004. Overview. Motivation Proposed solution LAS scheduling Analysis of LAS scheduling for jobs LAS scheduling in packet networks Differentiated LAS-based policies

york
Download Presentation

QoS Support in Edge Routers

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. PhD Thesis Defense QoS Support in Edge Routers Idris A. Rai Institut Eurecom/Telecom Paris France 15th September 2004

  2. Overview • Motivation • Proposed solution • LAS scheduling • Analysis of LAS scheduling for jobs • LAS scheduling in packet networks • Differentiated LAS-based policies • Summary

  3. Motivation: Internet QoS • Original Internet: • Best-effort service • TCP: guarantees only packet delivery • Emerged applications • QoS architectures: • IntServ, DiffServ, MPLS, TE, etc • Scalability issues in core • Complex signaling protocols • Parameters hard to tune • Backward compatibility issue  Current Internet still offersonly a best-effortservice with TCP

  4. Motivation: Internet traffic • Emerged applications include • Web (short flows) and peer-to-peer (large flows) • Internet traffic measurements • Most of flows are short and a few largest ones (1%) contribute a large fraction of the total load • High variability property (HVP) of flow size distribution • We use coefficient of variation (C = /m) to indicate variability level of a distribution • Distributions that model HVP: Pareto, Bounded Pareto (BP), Weibull, etc

  5. Fraction of the total mass Percentile Motivation: Internet traffic High variability property

  6. Proposed solution • Given the HVP of Internet traffic • Give priority to the many short flows • Don’t penalize large flows too much • We propose using LAS scheduling • At the network edges routers • Keeping best-effort service at the network core

  7. Analytical approaches • Mathematical analysis • Analyze LAS for CUP/Server scheduling • Job:is a workload that arrives in the system all at once • Simulation • Evaluate LAS for packet networks • Flow:is a sequence of packets arriving in bursts • Modeling • We model policies for packet networks

  8. S(x)PS x S(x) Performance Metrics:X = flow/job size • Response time • Conditional mean: • Mean: • Slowdown:S(x) := T(x)/x • Conditional mean: • Mean: • Penalty metric Penalty region

  9. What is LAS scheduling? LAS: Least attained service • Gives service to the job with the least attained service of all • Favors short jobs and reduces the mean T(x) • In queuing theory: (a.k.a. FB and SET) • Never proposed for packet networks before • Known to penalize largest jobs a lot when analyzed under M/M/1 queue [Kleinrock Vol. 2]

  10. LAS scheduling:Response time • T(x)LAS :

  11. SRPT requires the knowledge of job sizes • LAS schedulingdoes not require to know the size • of the jobs Related previous work • Shortest Remaining Processing Time (SRPT) First • A policy thatfavors short jobs • An optimal policy • Unfairness analysis of SRPT [Mor Balter et. al] • Implementation in Web servers

  12. Outline • Analysis of LAS for jobs • Studying LAS under M/G/1 queue • Comparing LAS to other policies: • PS; penalty analysis, servers • FIFO; routers • SRPT; optimal • A study of LAS in packet networks • Proposing new differentiated LAS-based policies

  13. Exponential BP PS Expected Slowdown S(x) Job Size Penalty analysis S(x) under LAS • Penalty level decreases with increasing variability of a distribution

  14. SLAS/SPS Load  Upper Bound of SLAS/SPS SLAS/ SPS< S(x) LAS<, x for  < 1, and all distributions

  15. Lemma: Proof: mn:= nth moment

  16. Small C and  -> 1 TLAS> TFIFO TLAS< TFIFO Load  C Upper Bound of TLAS/TFIFO From Lemma TLAS= TFIFO TLAS /TFIFO

  17. Exp BP: HVP T(x)LAS/T(x)SRPT Percentile of job size distribution Comparison of LAS and SRPT T(x)LAS T(x)SRPT for distributions with HVP at all load values E[T(x)] LAS /E[T(x)]SRPT

  18. Outline • Analyzing of LAS for jobs • A study of LAS in packet networks • Impact of TCP and FIFO with droptail to short flows • Simulation results for LAS vs. FIFO • Analytical model of LAS in packet networks • What about long-lived flows under LAS? • Proposing new differentiated LAS-based policies

  19. Client Server Initiate TCP connection Request object Firstwindow = S/C RTT RTT RTT Secondwindow = 2S/C Thirdwindow = 4S/C time at client time at server TCP during Slow Start (SS) • The transfer time during SS is dominated by RTT • Short flows are transferred during Slow Start phase of TCP

  20. Impact of FIFO to RTT FIFO queue • Queuing delay can be high andprolongs the RTT • FIFO with droptail can lose packets from short flows p Arrivingpacket o RTT = 2 prop. delay + o + p

  21. Wo/2 Wo Window Size (packets) Timeout RTO~3sec Time Impact losses to short TCP flows Packet losses at Slow Start • Packet losses at SS prolong transfer times of short flows Packet loss at SS Fast retransmit 1

  22. Priorityqueue Highest Priority Pkt Lowest Priority Pkt LAS in packet networks First packet from a new flow coming to a full queue First packet from a new flow • LAS reduces queuing delay for the first packets of a flow •  Reduces RTT (for short TCP flows) RTT  2 prop. delay + p LAS Reduces loss rate for short flows  Avoids Timeouts and the use of RTO to recover losses  Reduces transfer time for short flows

  23. 10Mb/s, 10ms Clients S3 Servers 3Mb/s, 30ms LAS in packet networks R1 R0 Bottleneck link • LAS is implemented in ns2 • Feldman et. al. [Sigcomm’99] • Flow sizes: Pareto

  24. FIFO LAS FIFO LAS LAS in packet networks Transfer time Mean transfer time (sec) Percentile Flow size (packets) • LAS reduces the mean transfer time of short flows • It does not penalize the largest flows a lot

  25. Packet loss rates Loss rates Flow size (packets) FIFO LAS LAS in packet networks • Flow of sizes < 40 pkts are unaffected • Loss rates of large flows under LAS remain moderate

  26. LAS model in packet networks • Claim: Given that • the lead packets of flows arrive at a random point in time • no or low packet loss rate  1% then the model of LAS for packet networks  model for jobs if: Packets service instances *approximates* jobs service instances

  27. RTT 2 3 LAS model in packet networks A job of 3 service units 1 Arrival instances 2 3 Service instances 1 2 3 Time A TCP flow of 3 service units 1 1 Arrival instances Service instances 1 2 3 Time

  28. Analysis Simulation Mean transfer time (sec) Flow size (packets) LAS model validation • Excellent agreement

  29. C1 S1 C2 S2 10Mb/s,10ms Clients 3Mb/s, 30ms Servers C3 3Mb/s, 30 ms S3 R1 R0 C4 S4 10Mb/s, 10ms C5 S5 Performance of long-lived flows Sink Source R1 R0 Note: We consider extremely long flows

  30. Long-lived FTP flows • Mean throughput (packets/sec) The performance of long-lived flows under LAS deteriorates under high load values

  31. Outline • Analyzing LAS for jobs • A studying LAS in packet networks • Designing new differentiated LAS-based policies • Derive models • Validate the models

  32. Differentiated LAS-based models • LAS-based models • Preserving nice properties of LAS • 2 types of flows in LAS: • Ordinary (r) and Priority (p) flows • Ordinary flows:Use the same priority change as under plain LAS • Priority flows:Use a priority function P(x)

  33. LAS-linear Priority value LAS-log Packet number Differentiated LAS-based models • Models and Priority Functions: • Plain LAS : P(x) = x • LAS-fixed(k) : P(x) = k, k>1 • LAS-linear(k) : P(x) = x/k, k>1 • LAS-log(k) : P(x) = log2(x)1/kkR+ • Motivation of priority functions • Reducing the number of ordinary flows (resp. their packets) that a priority flow has to compete against

  34. Differentiated LAS-based models • The expression ofT(x)for type i{r,p} flow is: • The truncated nth moment for flow sizex q = ratio of priority flows

  35. 2-class LAS based models • Moments in differentiated LAS based models

  36. Analysis Simulation Mean transfer time (sec) Flow size (packets) Models validationLAS-linear(k = 5) Ordinary flows Priority flows Similar accuracy for other k values and for LAS-log(k) and LAS-linear(k)

  37. LAS-log(k=1) LAS-log(k=0.5) LAS Throughput (packets/sec) Simulation time (sec) Performance of FTP long-lived flow Load = 0.92, Link speed = 120packets/sec

  38. We analyzed the performance of LAS for different job size distributions Contributions • We studied the interaction of LAS and TCP • We proposed new scheduling policies • We derived analytical models of the policies in packet networks • We validated the policies using simulation

  39. Couldn’t be covered… • LAS in heterogeneous networks • Heterogeneous propagation delays • Heterogeneous transport protocols • Multiple congested routers • LAS-FCFS scheduling • LAS-FCFS differentiated architectures

  40. Outlook Discrepancy due to losses of packets from long flows • Modeling the impact of packet losses to analytical models LAS Inside LAS at loss rate = 3.6%

  41. Thank you

  42. Publications • “Performance modeling of LAS based scheduling policies in packet switched networks “Proceedings of ACM Sigmetrics 2003 • “Analysis of LAS scheduling for job size distributions with high variance” Proceedings of ACM Sigmetrics 2003 • “LAS scheduling approach to avoid bandwidth hogging in heterogeneous TCP networks“7th IEEE International Conference on High Speed Networks and Multimedia communications HSNMC'04 • “Size-based scheduling with differentiated services to improve response time of highly varying flows” 5th ITC Specialist Seminar, Internet Traffic Engineering and Traffic Management Wurzburg, Germany, July 2002. • “Analyzing the performance of TCP flows in packet networks with LAS schedulers” Submitted for a journal publication 2004

  43. Exponential job distribution • Theorems: For an exponential job size distribution SLAS≤ SPS TLAS = TPS

  44. Exponential BP PS Expected Slowdown E[S(x)]) Percentile of job size distribution Penalized Jobs vs. C • C≥ 6, less than 1% of largest jobs see a small penalty

  45. LAS at Overload ( ≥ 1) • For FIFO and PS, E[T(x)] = , for  ≥ 1 x • Theorem: • E[T(x)] <  for  ≥ 1 if x < xLAS() • For SRPT [Bansal and Harchol-Balter, Sigmetrics 2001] • E[T(x)] <  for  ≥ 1 if x < xSRPT ()

  46. FIFO LAS FIFO LAS LAS in packet networks Transfer time Mean transfer time (sec) Percentile Flow size (packets) • LAS reduces the mean transfer time of short flows • It does not penalize the largest flows a lot

  47. Foreground traffic: LAS Background traffic:FIFO LAS-FCFS architecture Incoming traffic Outgoing traffic • Compared to LAS, LAS-FCFS helps the largest jobs for job size distribution with high variability property • Moderate size jobs under LAS-FCFS suffer

  48. LAS-FCFS based differentiated architectures • Differentiated models • Classify traffic into high priority (HP) and low priority (LP) • Fixed priority LAS-FCFS • Extended LAS-FCFS • Differential LAS-FCFS

  49. HP Incoming traffic Outgoing traffic Classifier LAS-FCFS LAS-FCFS LP Fixed priority LAS-FCFS • Can eliminate penalty for high priority jobs • However: Short low priority jobs receive a heavy penalty • But: Short jobs experience very low response time under LAS

  50. Foreground traffic: LAS Incoming traffic Outgoing traffic HP Classifier LP Background traffic:FCFS Differential LAS-FCFS architecture • It guarantees low response time for all short flows • It can significantly improve the performance of high priority large jobs

More Related