Gang scheduling in multi-core clusters implementing migrations
Future Generation Computer Systems
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Multi-core nodes of parallel machines may only provide gradual performance improvement per application due to competition on resources like the cache. As shown in our earlier work, spreading out applications over as many nodes as possible or letting different applications with potentially complementary characteristics (semi time) share each node by allocating different cores to them may provide better performance. In the latter case, groups of jobs may be necessary to obtain balanced resource utilization due to different sizes of jobs. We present a scheduler G-LOMARC-TS which can match groups of jobs and consider both space- and time-sharing allocation. Since matchmaking may select jobs further down in the waiting queue, fairness in regards to possible delays of the other jobs is watched and delays are kept within certain bounds. This results in a large number of possible combinations. A number of heuristics to select the most promising combinations make it possible to deal with the NP-completeness of the problem. We show that our scheduler improves utilization of high-load phases by about 27% and subsequently average response times by about 36% (and 53% for long jobs) compared to space sharing scheduling for normal workloads. Additionally the scheduler can handle much higher workloads than a space-sharing scheduler.