User-Aware Metrics for Measuring Quality of Parallel Job Schedules
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Job Scheduling Strategies for Parallel Processing |
| MU Faculty or unit | |
| Citation | |
| Web | http://www.cs.huji.ac.il/~feit/parsched/jsspp14/p3-toth.pdf |
| Doi | http://dx.doi.org/10.1007/978-3-319-15789-4_6 |
| Field | Informatics |
| Keywords | Grid; Performance evaluation; Metrics; Queue-based scheduling; Fairness; User-aware scheduling |
| Description | The work presented in this paper is motivated by the challenges in the design of scheduling algorithms for the Czech National Grid MetaCentrum. One of the most notable problems is our inability to efficiently analyze the quality of schedules. While it is still possible to observe and measure certain aspects of generated schedules using various metrics, it is very challenging to choose a set of metrics that would be representative when measuring the schedule quality. Without quality quantification (either relative, or absolute), we have no way to determine the impact of new algorithms and configurations on the schedule quality, prior to their deployment in a production service. The only two options we are left with is to either use expert assessment or to simply deploy new solutions into production and observe their impact on user satisfaction. To approach this problem, we have designed a novel user-aware model and a~metric that can overcome the presented issues by evaluating the quality on a~user level. The model assigns an expected end time (EET) to each job based on a fair partitioning of the system resources, modeling users expectations. Using this calculated EET we can then compare generated schedules in detail, while also being able to adequately visualize schedule artifacts, allowing an expert to further analyze them. Moreover, we present how coupling this model with a job scheduling simulator gives us the ability to do an in-depth evaluation of scheduling algorithms before they are deployed into a production environment. |
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