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Quantifying the Properties of SRPT Scheduling

This research delves into web server scheduling policies, emphasizing the impact on user-perceived response time and unfairness. Background, methodology, results, and implications are discussed with a focus on SRPT.

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Quantifying the Properties of SRPT Scheduling

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  1. Quantifying the Properties of SRPT Scheduling Mingwei Gong and Carey Williamson Department of Computer Science University of Calgary

  2. Outline • Introduction • Background • Web Server Scheduling Policies • Related Work • Research Methodology • Simulation Results • Defining/Refining Unfairness • Quantifying Unfairness • Summary, Conclusions, and Future Work

  3. Introduction • Web: large-scale, client-server system • WWW: World Wide Wait! • User-perceived Web response time is composed of several components: • Transmission delay, propagation delay in network • Queueing delays at busy routers • Delays caused by TCP protocol effects (e.g., handshaking, slow start, packet loss, retxmits) • Queueing delays at the Web server itself, which may be servicing 100’s or 1000’s of concurrent requests • Our focus in this work: Web request scheduling

  4. Example Scheduling Policies • FCFS: First Come First Serve • typical policy for single shared resource (“unfair”) • e.g., drive-thru restaurant; Sens playoff tickets • PS: Processor Sharing • time-sharing a resource amongst M jobs • each job gets 1/M of the resources (equal, “fair”) • e.g., CPU; VM; multi-tasking; Apache Web server • SRPT: Shortest Remaining Processing Time • pre-emptive version of Shortest Job First (SJF) • give resources to job that will complete quickest • e.g., ??? (express lanes in grocery store)(almost)

  5. Related Work • Theoretical work: • SRPT is provably optimal in terms of mean response time and mean slowdown (“classical” results) • Practical work: • CMU: prototype implementation in Apache Web server. The results are consistent with theoretical work. • Concern: unfairness problem (“starvation”) • large jobs may be penalized (but not always true!)

  6. Related Work (Cont’d) • Harchol-Balter et al. show theoretical results: • For the largest jobs, the slowdown asymptotically converges to the same value for any preemptive work-conserving scheduling policies (i.e., for these jobs, SRPT, or even LRPT, is no worse than PS) • For sufficiently large jobs, the slowdown under SRPT is only marginally worse than under PS, by at most a factor of 1 + ε, for small ε > 0. [M.Harchol-Balter, K.Sigman, and A.Wierman 2002], “Asymptotic Convergence of Scheduling Policies w.r.t. Slowdown”, Proceedings of IFIP Performance 2002, Rome, Italy, September 2002

  7. Related Work (Cont’d) • [Wierman and Harchol-Balter 2003]: SJF FSP LAS Always Fair Always Unfair Sometimes Unfair PS FCFS PLCFS LRPT SRPT [A. Wierman and M.Harchol-Balter 2003], (Best Paper) “Classifying Scheduling Policies w.r.t. Unfairness in an M/GI/1”, Proceedings of ACM SIGMETRICS, San Diego, CA, June 2003

  8. “asymptotic convergence” “crossover region” (mystery hump) 1 1-p x y A Pictorial View 8 PS Slowdown SRPT 1 0 8 Job Size

  9. Research Questions • Do these properties hold in practice for empirical Web server workloads? (e.g., general arrival processes, service time distributions) • What does “sufficiently large” mean? • Is the crossover effect observable? • If so, for what range of job sizes? • Does it depend on the arrival process and the service time distribution? If so, how? • Is PS (the “gold standard”) really “fair”? • Can we do better? If so, how?

  10. Overview of Research Methodology • Trace-driven simulation of simple Web server • Empirical Web server workload trace (1M requests from WorldCup’98) for main expts • Synthetic Web server workloads for the sensitivity study experiments • Probe-based sampling methodology • Estimate job response time distributions for different job size, load level, scheduling policy • Graphical comparisons of results • Statistical tests of results (t-test, F-test)

  11. Simulation Assumptions • User requests are for static Web content • Server knows response size in advance • Network bandwidth is the bottleneck • All clients are in the same LAN environment • Ignores variations in network bandwidth and propagation delay • Fluid flow approximation: service time = response size • Ignores packetization issues • Ignores TCP protocol effects • Ignores network effects • (These are consistent with SRPT literature)

  12. Performance Metrics • Number of jobs in the system • Number of bytes in the system • Normalized slowdown: • The slowdown of a job is its observed response time divided by the ideal response time if it were the only job in the system • Ranges between 1 and  • Lower is better

  13. Empirical Web Server Workload

  14. ... 3 2 Number of Jobs in the System 1 0.000315 0.001048 ... 5000 4000 Number of Bytes in the System 3000 0.000315 0.001048 Time Preliminaries: An Example Jobs in System Bytes in System

  15. The “byte backlog” is the same for each scheduling policy The busy periods are the same for each policy. Observations: • The distribution of the number of jobs in the system is different

  16. General Observations (Empirical trace) Load 50% Load 80% Load 95% Marginal Distribution (Num Jobs in System) for PS and SRPT: differences are more pronounced at higher loads

  17. Objectives (Restated) • Compare PS policy with SRPT policy • Confirm theoretical results in previous work (Harchol-Balter et al.) • For the largest jobs • For sufficiently large jobs • Quantify unfairness properties

  18. PS PS PS Probe-Based Sampling Algorithm • The algorithm is based on PASTA (Poisson Arrival See Time Average) Principle. Slowdown (1 sample) Repeat N times

  19. Probe-based Sampling Algorithm For scheduling policy S =(PS, SRPT, FCFS, LRPT, …) do For load level U = (0.50, 0.80, 0.95) do For probe job size J = (1B, 1KB, 10KB, 1MB...) do For trial I= (1,2,3… N) do Insert probe job at randomly chosen point; Simulate Web server scheduling policy; Compute and record slowdown value observed; end of I; Plot marginal distribution of slowdown results; end of J; end of U; end of S;

  20. Load 50% Load 80% Load 95% Example Results for 3 KB Probe Job

  21. Load 50% Load 80% Load 95% Size 100K Example Results for 100 KB Probe Job

  22. Load 50% Load 80% Load 95% Example Results for 10 MB Probe Job

  23. Statistical Summary of Results

  24. Two Aspects of Unfairness • Endogenous unfairness: (SRPT) • Caused by an intrinsic property of a job, such as its size. This aspect of unfairness is invariant • Exogenous unfairness: (PS) • Caused by external conditions, such as the number of other jobs in the system, their sizes, and their arrival times. • Analogy: showing up at a restaurant without a reservation, wanting a table for k people

  25. PS is “fair” Sort of! Exogenous unfairness dominant Observations for PS

  26. Endogenous unfairness dominant Observations for SRPT

  27. Asymptotic Convergence? Yes!

  28. Linear Scale Log Scale Illustrating the crossover effect (load=95%) 3M 3.5M 4M

  29. Crossover Effect? Yes!

  30. Summary and Conclusions • Trace-driven simulation of Web server scheduling strategies, using a probe-based sampling methodology (probe jobs) to estimate response time (slowdown) distributions • Confirms asymptotic convergence of the slowdown metric for the largest jobs • Confirms the existence of the “cross-over effect” for some job sizes under SRPT • Provides new insights into SRPT and PS • Two types of unfairness: endogenous vs. exogenous • PS is not really a “gold standard” for fairness!

  31. Ongoing Work • Synthetic Web workloads • Sensitivity to arrival process (self-similar traffic) • Sensitivity to heavy-tailed job size distributions • Evaluate novel scheduling policies that may improve upon PS (e.g., FSP, k-SRPT, …)

  32. Sensitivity to Arrival Process • A bursty arrival process (e.g., self-similar traffic, with Hurst parameter H > 0.5) makes things worse for both PS and SRPT policies • A bursty arrival process has greater impact on the performance of PS than on SRPT • PS exhibits higher exogenous unfairness than SRPT for all Hurst parameters and system loads tested

  33. Sensitivity to Job Size Distribution • SRPT loves heavy-tailed distributions: the heavier the tail the better! • For all Pareto parameter values and all system loads considered, SRPT provides better performance than PS with respect to mean slowdown and standard deviation of slowdown • At high system load (U = 0.95), SRPT has more pronounced endogenous unfairness than PS

  34. Thank You!Questions? For more information: M. Gong and C. Williamson, “Quantifying the Properties of SRPT Scheduling”, to appear, Proceedings of IEEE MASCOTS, Orlando, FL, October 2003 Email: {gongm,carey}@cpsc.ucalgary.ca

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