A Case for a Multifaceted Fairness Model: An Overview of Fairness Methods for Job Queuing and Scheduling
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Proceedings |
| Conference | Memics 2014 |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | Scheduling; Fairshare; Simulation |
| Attached files | |
| Description | Job scheduling for HPC and Grid-like systems, while being a heavily studied subject, suffers from a particular disconnect between theoretical approaches and practical applications. Most production systems still rely on a small set of rather conservative scheduling policies. One of the areas that tries to bridge the world of scientific research and practical application is the study of fairness. Fairness in a system has strong implications on customer satisfaction, with psychological studies suggesting that perceived fairness is generally even more important than the quality of service. This paper provides an overview of different approaches for handling fairness in a job scheduling/queuing system. We start with analytic approaches that rely on statistical modeling and try to provide strong categorization and ordering of various scheduling policies according to their fairness. Following that we provide an overview of recent advancements that rely on simulations and use high resolution analysis to extract fairness information from realistic job traces. As a conclusion to this article, we propose a new direction for research. We propose a novel multifaceted fairness approach, i.e., a combination of different fairness models inside a single system, that could better capture the heterogeneous fairness-related requirements of different users in the system. It could serve as a solution to the shortcomings of some of the methods presented in this paper. |
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