Name
Abstract | ||
|---|---|---|
It is common nowadays that multiple cores reside on the same chip and share the on-chip cache. Resource sharing may cause performance degradation of the co-running jobs. Job co-scheduling is a technique that can effectively alleviate the contention. Many co-schedulers have been developed in the literature, but most of them do not aim to find the optimal co-scheduling solution. Being able to determine the optimal solution is critical for evaluating co-scheduling systems. Moreover, most co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in some situations. This paper aims to tackle these issues. In this paper, a graph-based method is developed to find the optimal co-scheduling solution for serial jobs, and then the method is extended to incorporate parallel jobs. The extensive experiments have been conducted to evaluate the effectiveness and efficiency of the proposed co-scheduling algorithms. The results show that the proposed algorithms can find the optimal co-scheduling solution for both serial and parallel jobs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MASCOTS.2014.16 | MASCOTS |
Keywords | DocType | ISSN |
job co-scheduling optimization,optimisation,co-scheduling,parallel processing,scheduling,parallel jobs,multicore,on-chip cache,multicore computers,serial jobs,multiprocessing systems,parallel application,optimal co-scheduling solution,resource sharing,graph theory,graph-based method,schedules,degradation,time complexity,multicore processing,algorithm design and analysis | Conference | 1526-7539 |
Citations | PageRank | References |
3 | 0.39 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huanzhou Zhu | 1 | 11 | 2.19 |
| Ligang He | 2 | 542 | 56.73 |
| Stephen A. Jarvis | 3 | 1073 | 87.04 |