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Improving Process Scheduling Though Machine Learning Techniques
Transcript of Improving Process Scheduling Though Machine Learning Techniques
Improving Process Scheduling
Though Machine Learning
Learning From the Past
Learning From Your Peers
Historical Profiler Example
Researcher: Richard Gibbons
- stores and retrieves historical dat
Execution Time Objects
- stores information about specific jobs and is returned whenever the Scheduler queries about specific jobs
Even imperfect records lead to improved performance.
However, must maintain permanent memory usage.
Researchers: Warren Smith,
Valerie Taylor, and Ian Foster
Developed a method for deriving the run times of parallel applications from the run times of similar applications that have executed in the past.
Resulted in better estimations than Gibbons method.
Still Works ^^^
Researchers: Hao Shen, Ying Tan, Jun Lu, Qing Wu, and Qinru Qiu
Developed a learning system that operates at runtime,
requiring no past knowledge.