1 Size-Based Scheduling Policies with Inaccurate Scheduling Information Dong Lu *, Huanyuan Sheng +, Peter A. Dinda * * Prescience Lab, Dept. of Computer Science + Dept. of Industrial Engineering & Management Science Northwestern University Evanston, IL USA

2 Outline Review of size-based scheduling Motivation Simulation Setup Simulation Results New applications Research Summary by subjects

3 Non-size-based scheduling FCFS, PS, etc. FCFS: First Come First Serve –Intuitive –Easiest to implement PS: Processor Sharing –Fair: all jobs accept equal resources –Also easy to implement Problem: Unaware of job size information, which results in big mean response time

4 Review of size-based scheduling SRPT, FSP, etc. Utilize the job size (processing time, service time) information for scheduling –Optimal in mean response time –Fair? –Easy to implement? We use Job Size to refer to the Processing Time (Service Time) of the job

5 Shortest Remaining Processing Time (SRPT) Always serve the job with minimum remaining processing time first, Preemptive scheduling Yields minimum mean response time [Schrage, Operations Research, 1968] Performance gains of SRPT over PS do not usually come at the expense of large jobs, in other words, it is Fair for heavy-tail job size distribution [Bansal and Harchol-Balter, Sigmetrics ‘01] Easy to implement? –With accurate a priori job size information, YES –Otherwise, NO

6 Fair Sojourn Protocol (FSP) Combined SRPT with PS, preemptive scheduling Mean response time is close to that of SRPT; and more fair than PS [Friedman, et al, Sigmetrics ‘03] Easy to implement? –With accurate a priori job size information, YES –Otherwise, NO

7 Motivation Size-based scheduling requires accurate knowledge of job sizes In practice, a priori job size information is not always available All the previous work assumes perfect knowledge of job sizes a priori How does performance depend on quality of job size information?

8 Correlation We study the performance of Size-based schedulers as a function of the correlation coefficient (Pearson’s R) between actual job sizes and estimated job sizes.

9 Outline Review of size-based scheduling Motivation Simulation Setup Simulation Results New applications Research Summary by subjects

10 Simulation Setup: Trace generator Trace Generator Correlation (Pearson’s R) Distribution ADistribution B X Y Correlated random pairs of X and Y X has distribution A Y has distribution B X and Y are correlated to R

11 Simulation Setup: Trace generator Algorithm: “Normal-To-Anything” –First developed by Cario and Nelson, on INFORMS Journal on Computing 10, 1 (1998). –We simplified the algorithm and first introduced it into the simulation studies of computer systems

12 Scatter plot of example traces R=0.13 R=0.78 Y X Y X

13 Simulation Setup: Performance metrics Performance metrics –Mean response time: Sojourn time, Turn-around time –Slowdown: the ratio of response time to its size. Fairness metric

14 Simulation Setup: Simulator Simulator –Written in C++ –Supports M/G/1 and G/G/n/m queuing model Simulator validation –Little’s law –Repeat the simulations in the FSP paper [Friedman, et al, Sigmetrics ‘03] –Compare with available theoretical results [Bansal and Harchol-Balter, Sigmetrics ‘01]

15 Simulation Setup: Scheduling Policies PS: Processor sharing Size-based scheduling policies –SRPT: Ideal SRPT scheduler –SRPT-E: SRPT scheduler using estimated job size –FSP: Ideal Fair Sojourn Protocol –FSP-E: FSP scheduler using estimated job size Each simulation is repeated 20 times and we present the average

16 Outline Review of size-based scheduling Motivation Simulation Setup Simulation Results New applications Research Summary by subjects

17 Simulation Results: Mean response time

18 Simulation Results: Slowdown (R=0.0224)

19 Simulation Results: Slowdown (R=0.239)

20 Simulation Results: Slowdown (R=0.4022)

21 Simulation Results: Slowdown (R=0.5366)

22 Simulation Results: Slowdown (R=0.7322)

23 Simulation Results: Slowdown (R=0.9779)

24 Simulation Results: Conclusions Performance heavily depends on correlation –SRPT-E and FSP-E can outperform PS given an effective job size estimator Crossover point of performance metrics is a function of correlation –Also of job size distributions (See TR NWU-CS-04-33)

25 Outline Review of size-based scheduling Motivation Simulation Setup Simulation Results New applications Research Summary by subjects

26 New Applications: Web server scheduling (TR NWU-CS-04-33) Is file size a good estimator of a job’s service time (processing time)? Not Really (R  0.14) Service time (wall clock time) File Size

27 New Applications: Web server scheduling Domain-based estimator: much more accurate prediction of the service time at low overhead

28 New Applications: P2P server side scheduling (LCR ’04) “Server side” of current file sharing P2P applications superficially similar to web server –Both send back files upon requests. However, P2P application can’t even know the file size accurately a priori –Partial downloads Our ongoing work shows that SRPT-E performs well using our time-series based job size estimators.

29 New Applications: Network backup system scheduling Incremental backup copies only the files that have been created or modified since a previous backup With Incremental backup, the actual job sizes is difficult to know until the backup finishes We believe that SRPT-E or FSP-E can be applied with time series based job size predictors

30 Summary Performance of size-based scheduling policies depends on correlation between size estimates and actual sizes –Fairness, mean response time, etc. Estimator must preserve ordering of job sizes for high performance –Performance degrades as correlation degrades Effective new estimators for Web and P2P

31 For More Information Prescience Laboratory – Home Page of Dong Lu –

32 Outline Review of size-based scheduling Motivation Simulation Setup Simulation Results New applications Research Summary by subjects

33 Research Summary by subjects Grid Computing Internet Measurement and Prediction Queuing and Scheduling Fat-Tree Based End System Multicast Wireless Ad Hoc Networks Incentivized Protocol Design for Peer-to-Peer Systems Parallel Computing

34 Grid Computing Dong Lu, Peter Dinda, “GridG: Generating Realistic Computational Grids”, ACM SIGMETRICS Performance Evaluation Review (Per), Volume 30, Number 4, Dong Lu, Peter Dinda, “Synthesizing Realistic Computational Grids,” Proceedings of the 15th ACM/IEEE Supercomputing (SC 2003), Phoenix, AZ, November Peter Dinda, Dong Lu, “Nondeterministic queries in a relational Grid information service”, Proceedings of the 15th ACM/IEEE Supercomputing (SC 2003), Phoenix, AZ, November Dong Lu, Peter Dinda, Jason Skicewicz ”Scoped and Approximated queries in a relational Grid Information Service”, Proceedings of 4th IEEE/ACM International Workshop on Grid Computing (Grid 2003), November, 2003, Phoenix, AZ. Bin Lin, Peter Dinda, Dong Lu, ”User-driven Scheduling of Interactive Virtual Machines”, Proceedings of Grid 2004, PITTSBURGH, PA, November, 2004.

35 Internet Measurement and Prediction Dong Lu, Y. Qiao, Peter Dinda, and F. Bustamante, ”Characterizing and Predicting TCP Throughput on the Wide Area Network”, Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS 2005), June 2005, Columbus, Ohio. To appear. Dong Lu, Yi Qiao, Peter Dinda, Fabian Bustamante, ”Modeling and Taming Parallel TCP on the Wide Area Network”, Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2005), April 4-8, 2005, Denver, Colorado.

36 Queuing and Scheduling Dong Lu, Huanyuan Sheng, Peter Dinda, “Size-Based Scheduling Policies with Inaccurate Scheduling Information,” Proceedings of MASCOTS 2004, October 2004, Volendam, The Netherlands. Dong Lu, Peter Dinda, Yi Qiao, Huanyuan Sheng, Fabian Bustamante, “Applications of SRPT Scheduling with Inaccurate Scheduling Information”, (short paper) Proceedings of MASCOTS 2004, October 2004, Volendam, The Netherlands. Yi Qiao, Dong Lu, Fabian Bustamante, Peter Dinda, ”Looking at the Server-Side of Peer-to-Peer Systems”, Proceedings of the 7th ACM Workshop on Languages, Compilers, and Run-time Systems for Scalable Computers (LCR 2004), October 2004, Houston, Texas. Dong Lu, Huanyuan Sheng, and Peter Dinda, “Effects and Implications of File Size/Service Time Correlation on Web Server Scheduling Policies”, Technical Report NWU-CS-04-33, Department of Computer Science, Northwestern University, April, In Submission.

37 Fat-Tree Based End System Multicast: FatNemo Stefan Birrer, Dong Lu, Fabian Bustamante, Yi Qiao, Peter Dinda, “FatNemo: Building a Resilient Multi-Source Multicast Fat-Tree”, Proceedings of the Ninth International Workshop on Web Content Caching and Distribution (WCW 2004), October 2004, Beijing, China. Also appeared in LNCS, Vol. 3293/2004, pp –Long version in submission

38 Wireless Ad Hoc Networks Dong Lu, Haitao Wu, Qian Zhang, Wenwu Zhu, “PARS: Stimulating Cooperation for Power-Aware Routing in Ad-Hoc Networks”. Proceedings of the 40th IEEE International Conference on Communications (ICC 2005), May 2005, Seoul, Korea. To appear.

39 Incentivized Protocol Design for Peer- to-Peer Systems Dong Lu, Yi Qiao, Peter Dinda, Fabian Bustamante, “MultiTorrents: Bandwidth Optimized Hybrid Multicast for Incentivized P2P File Sharing”. In Submission.

40 Parallel Computing Dong Lu, Peter Dinda ”Virtualized Audio: A Highly adaptive Interactive High Performance Computing Application”, Proceedings of the 6th Workshop on Languages, Compilers, and Run-time Systems for Scalable Computers (LCR 2002), Washington, DC, Also to appear in LNCS.