Presentation on theme: "Quantifying the Properties of SRPT Scheduling Mingwei Gong and Carey Williamson Department of Computer Science University of Calgary."— Presentation transcript:
Quantifying the Properties of SRPT Scheduling Mingwei Gong and Carey Williamson Department of Computer Science University of Calgary
July 22, Outline Introduction Background Web Server Scheduling Policies Related Work Research Methodology Simulation Results Defining/Refining Unfairness Quantifying Unfairness Summary, Conclusions, and Future Work
July 22, 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 100s or 1000s of concurrent requests Our focus in this work: Web request scheduling
July 22, 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)
July 22, 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!)
July 22, Related Work (Contd) 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
July 22, Related Work (Contd) [Wierman and Harchol-Balter 2003]: [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 Always Unfair Sometimes Unfair Always Fair FCFS LAS LRPT FSP PLCFS SRPT SJF PS
July 22, Job Size Slowdown PS SRPT 0 8 A Pictorial View crossover region (mystery hump) asymptotic convergence x y p
July 22, 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?
July 22, Overview of Research Methodology Trace-driven simulation of simple Web server Empirical Web server workload trace (1M requests from WorldCup98) 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)
July 22, 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)
July 22, 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
July 22, Empirical Web Server Workload 1998 WorldCup: Internet Traffic Archive: ItemValue Trace Duration861 sec Total Requests1,000,000 Unique Documents5,549 Total Transferred Bytes3.3 GB Smallest Transfer Size (bytes)4 Largest Transfer Size (bytes)2,891,887 Median Transfer Size (bytes)889 Mean Transfer Size (bytes)3,498 Standard Deviation (bytes)18,815
July 22, TIMESTAMPSIZE Preliminaries: An Example Number of Jobs in the System Number of Bytes in the System Time Jobs in System Bytes in System...
July 22, Observations: The byte backlog is the same for each scheduling policy The busy periods are the same for each policy. The distribution of the number of jobs in the system is different
July 22, Marginal Distribution (Num Jobs in System) for PS and SRPT: differences are more pronounced at higher loads General Observations (Empirical trace) Load 50%Load 80%Load 95%
July 22, 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
July 22, Probe-Based Sampling Algorithm The algorithm is based on PASTA (Poisson Arrival See Time Average) Principle. PS Slowdown (1 sample) Repeat N times
July 22, 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;
July 22, Load 50%Load 80%Load 95% Example Results for 3 KB Probe Job
July 22, Load 50%Load 80%Load 95% Size 100K Example Results for 100 KB Probe Job
July 22, Load 50%Load 80%Load 95% Example Results for 10 MB Probe Job
July 22, Statistical Summary of Results
July 22, 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
July 22, Observations for PS Exogenous unfairness dominant PS is fairSort of!
July 22, Observations for SRPT Endogenous unfairness dominant
July 22, Asymptotic Convergence? Yes!
July 22, M 3.5M 4M Linear ScaleLog Scale Illustratingthecrossovereffect(load=95%)
July 22, Crossover Effect? Yes!
July 22, 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!
July 22, 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, …)
July 22, 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
July 22, 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
July 22, Thank You! Questions? M. Gong and C. Williamson, Quantifying the Properties of SRPT Scheduling, to appear, Proceedings of IEEE MASCOTS, Orlando, FL, October 2003 For more information: