A thesis based around saving response time costs as well as respecting electrical costs of a homogenous multi-server system. / In this thesis we consider the impacts of energy costs as they relate to Size Interval Task Assignment Equally--loaded (SITA-E) systems. We find that given systems which have small and large jobs being processed (high variance systems) we could in some cases find savings in terms of energy costs and in terms of lowering the mean response times of the system. How we achieve this is by first working from SITA-E, wherein servers are always on to Electrically Aware SITA-E (EA-SITA-E) by seeing if it is beneficial to make any of our servers rotate between being on and being off as needed. When most beneficial to do so we will turn off some of the servers in question, after this is completed we reallocate some of the jobs that are on the servers that we decide will be cycling to servers that will remain on indefinitely to better use their idle time. This also lowers the mean response time below what we originally saw with SITA-E, by lowering the variance in the sizes of jobs seen by the servers with the longest jobs. These long--job servers are by far the most impacted by the variance of the sizes of the jobs, so it is very desirable to lower this variance. The algorithm contained here can provide benefits in terms of both energy costs and mean response time under some specific conditions. Later we discuss the effect of errors in our assumed knowledge of task sizes. This research contributes methodology that may be used to expand on EA-SITA-E system design and analysis in the future. / Thesis / Master of Science (MSc) / The intention of this research is to be able to improve on existing size interval task-based assignment policies. We try to improve by turning servers off at key times to save energy costs, while not sacrificing too greatly in terms of mean response time of the servers, and in some cases even improving the mean response time through an intelligent re-balancing of the server loads.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27531 |
| Date | January 2022 |
| Creators | Moore, Maxwell |
| Contributors | Down, Douglas, Computer Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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