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CS Research – A Case Study

CS Research – A Case Study. Exploiting Process Lifetime Distributions for Dynamic Load Balancing. The Players. Two Ph.D. students at Berkeley Mor Harchol-Balter Functional Analysis, Formal, Mathematical Berkeley 1990-1996 First Paper 1994 Allen B. Downey Systems Research

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CS Research – A Case Study

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  1. CS Research – A Case Study Exploiting Process Lifetime Distributions for Dynamic Load Balancing

  2. The Players • Two Ph.D. students at Berkeley • Mor Harchol-Balter • Functional Analysis, Formal, Mathematical • Berkeley 1990-1996 • First Paper 1994 • Allen B. Downey • Systems Research • MS MIT 1990, Berkeley 1991-1997 • First Paper 1993 • Not part of either players Ph.D. Thesis!

  3. How it started • ELZ88 • The limited performance benefits of migrating active processes for load sharing • “There are likely no conditions under which migration could yield major performance improvements beyond those offered by non-migratory load sharing…”

  4. First Steps • Find the hole in ELZ’s argument • Its based on an “unusual” distribution • Is this the observed distribution in 1994? • Collect and Analyze • Something smells….

  5. Interim Status • ELZ Model is limited • ELZ metric is mean residence time, not mean slowdown • ELZ process lifetime distribution is artificial • Lots of zero length processes

  6. Tech Report • Not enough data for an article • It’s a work-in-progress • A note on “The Limited Performance Benefits of Migrating Active Processes for Load Sharing” • Document their results, no conclusions except that ELZ88 looks wrong

  7. Next Steps • Collect data • Identify the empirical distributions • Define system model • Propose Algorithm • Simulate, Analyze • Repeat until done

  8. Collect Data • So many machines, what to choose… • Start with local resources • 7 local time-sharing servers • I.E. Mangal, Pita, Inferno, Sands • Use available tools • lastcomm – print out information about previously executed commands

  9. Identify Empirical Distributions • Graph data • Matlab, ploticus, gnuplot • Eyeball against known distributions • “Looks to me like a lognormal” • Use statistical tests to determine fit • Iteratively weighted least-squares-fit • Document it...

  10. Define System Model • What is the definition of the system that you will analyze? • Real world systems are too complicated • Choose a model that has just the right amount of simplification • Too simple  obvious or incorrect results • Too complicated  much harder analysis

  11. Propose Algorithm • The paper presents the one that worked • Start with something • Throw it away • Complicate: Add parameters • Simplify: Remove parameters • Refine until • It works • You can explain it

  12. Simulate • Build a simulator of your model • Use Java, C++, Python, whatever • NS-2 • Prove empirically that the algorithm • Works • Is better than your competition

  13. Analyze • Collect data points • Statistics • Mean, Std-Dev, etc. • Sensitivity to parameters

  14. Compare to Competition • Choose the competition so that you look good • Change their algorithm to work in your model • Compare the options (using restricted parameters)

  15. Write Paper • Now document your work

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