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Information and Scheduling: What's available and how does it change

This talk explores the relationship between scheduling algorithms and the available information, discussing how data changes and what to do about it.

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Information and Scheduling: What's available and how does it change

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  1. Information and Scheduling: What's available and how does it change Jennifer M. Schopf Argonne National Lab

  2. Information and Scheduling • How a scheduler work is closely tied to the information available • Choice of algorithm dependent on accessible data

  3. This Talk • What approaches expect form information • What data is actually available, and some open questions • How data changes • What to do about changing data

  4. NB • I’m speaking (pessimistically) from my own background • We’ve heard some talks earlier today (for example PACE) which address some of these problems • I still think these are interesting open issues to think about

  5. Information systems(NOTE: taken from my standard MDS2 talk) • Information is always old • Time of flight, changing system state • Need to provide quality metrics • Distributed system state is hard to obtain • Information is not contemporaneous (thanks j.g.) • Complexity of global snapshot • Components will fail • Scalability and overhead • Approaches are changed for scalability, this will affect the information available

  6. Scheduling approaches assume • A lot of data is available • All information is accurate • Values don’t change

  7. What some people expect • Perfect bandwidth info • Number of operations in an application • Scalar value of computer “power” • Mapping of “power” to applications • Perfect load information

  8. Bandwidth data • Network Weather Service (Wolski, UCSB) • 64k probe BW data • Latency data • Predictions • Pinger (Les Cotrell, SLAC) • Create long term baselines for expectations on means/medians and variability for response time, throughput, packet loss • Predicting TCP performance • Allen Downey • http://allendowney.com/research/tcp/ • But what do Grid applications need?

  9. LBL-ANL GridFTP (approximately 400 transfers at irregular intervals) end-to-end bandwidth and NWS (approximately 1,500 probes every five minutes) probe bandwidth for the two-week August’01 dataset. Perfect Bandwidth Data 64 k probes don’t look like large file transfers

  10. Predicting Large File Transfers • Vazhkudai and Schopf: use GridFTP logs and some background data - NWS, ioStat (HPDC 2002) • Error rate of ~15% • M. Faerman A. Su, R. Wolski,andF. Berman (HPDC 99) • Similar results for SARA data • Hu and Schopf: use an AI learning technique on GridFTP log files only (not published yet) • Picks best place to get a file from 60-80% of time, using averages only gives you ~50% “best chosen” • This topic needs much more study!

  11. Data GenerallyAvailable From an Application • What some scheduling approaches want: • Number of ops in an application • Exact execution time on a platform • Perfect models of applications

  12. Application DataCurrently Available • Bad models of applications • No models of applications • Some work (Propehsy, Taylor at Texas A&M) does logging to create models • Many interesting applications have non-deterministic run times • User estimates of application run time (historically) off by 20%+ • We need to be able to figure out ways to do predictions of application run times WITHOUT models

  13. Scalar value of computer “power” • MDS2 gives me: • CPU vendor, model and version • CPU speed • OS name, release and version • RAM size • Node count • CPU count • Where is “compute power” in this data?

  14. What is compute “power” • I could get benchmark data, but what’s the right benchmark(s) to use? • Computer “power” simply isn’t scalar, especially in a Grid environment • Goal is really to understand how an application will run on a machine Given three different benchmarks, 3 different platforms will perform very differently – one best on BM1, another best on BM2

  15. Mapping “power” to applications • Many scheduling approaches assume “power” is a scalar – just multiply it by the set application time and we’re set • Only problem: • Power isn’t a scalar • No one knows absolute application run times • Mapping will NOT be straight forward • We need a way to estimate application time on a contended system

  16. Perfect Load Information • MDS2 gives me: • Basic queue data • Host load 5/10/15 min avg • Last value only

  17. Load Predictions • Network weather service • 12+ prediction techniques • Work on any time series • Expect regularly arriving data • Only a prediction of the next value • *I* want to know what load is going to be like in 20 mins • Or the AVERAGE over the next 20 mins?

  18. Information and Scheduling • What approaches expect us to have • What we actually have access to • How it changes • What to do about changing data

  19. Dedicated SOR Experiments • Platform- 2 Sparc 2’s. 1 Sparc 5, 1 Sparc 10 • 10 mbit ethernet connection • Quiescent machines and network • Prediction within 3% before memory spill

  20. Non-dedicated SOR results • Available CPU on workstations varied from .43 to .53

  21. SOR with Higher Variancein CPU Availability

  22. Improving predictions • Available CPU has range of 0.48 +/- 0.05 • Prediction should also have a range

  23. Scheduling needsto consider variance • Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments • Lingyun Yang, Jennifer M. Schopf, Ian Foster • To appear at SC'03, November 15-21, 2003, Phoenix, Arizona, USA • www.mcs.anl.gov/~jms/Pubs/lingyun-SC-scheduling.pdf

  24. Scheduling with Variance • Summary: Scheduling with variance can give better mean performance and less variance in overall execution time

  25. Lessons: • We need work predicting large file transfers – NOT bandwidth • We need to be able to figure out ways to do predictions of application run times WITHOUT models • We need predictions over time periods – not just a next value • We need a way to represent “power” of a machine, that takes variance into account • We need a way to map power to application behavior • We need better scheduling approaches that take variance into account

  26. Contact Information • Jennifer M. Schopf • jms@mcs.anl.gov • www.mcs.anl.gov/~jms • Links to some of the publications mentioned • Links to the co-edited book “Grid resource Management: State of the Art and Future Trends”

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