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Smart- Suspend-and-Resume for Rarity Scheduling
BibTeX
@MISC{Mary_smart-suspend-and-resume,
author = {William Mary and Feng Yan and Shannon Hughes and Alma Riska and Evgenia Smirni and Feng Yan and Shannon Hughes and Alma Riska and Evgenia Smirni},
title = {Smart- Suspend-and-Resume for Rarity Scheduling},
year = {}
}
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Abstract
Abstract—It is common nowadays to architect and design scaled-out systems with off-the-shelf computing components operated and managed by off-the-shelf open-source tools. While web services represent the critical set of services offered at scale, big data analytics is emerging as a preferred service to be colocated with cloud web services at a lower priority raising the need for off-the-shelf priority scheduling. In this paper we report on the perils of Linux priority scheduling tools when used to differentiate between such complex services. We demonstrate that simple priority scheduling utilities such as nice and ionice can result in dramatically erratic behavior. We provide a remedy by proposing an autonomic priority scheduling algorithm that adjusts its execution parameters based on on-line measurements of the current resource usage of critical applications. Detailed experimentation with a user-space prototype of the algorithm on a Linux system using popular benchmarks such as SPEC and TPC-W illustrate the robustness and versatility of the proposed technique, as it provides consistency to the expected performance of a high-priority application when running simultaneously with multiple low priority jobs. I.
Keyphrases
smart suspend-and-resume rarity scheduling execution parameter preferred service web service current resource usage complex service erratic behavior off-the-shelf priority scheduling on-line measurement big data analytics autonomic priority linux system high-priority application cloud web service simple priority common nowadays scaled-out system multiple low priority job critical set off-the-shelf open-source tool linux priority scheduling tool user-space prototype popular benchmark expected performance critical application
