Characteristics of fairness metrics and their effect on perceived scheduler effectiveness

Abstract

Parallel job schedulers are mostly evaluated using performance metrics. Deductions however can be misleading due to selective job starvation (unfairness). To choose a better scheduler, therefore, there is a need to compare schedulers for fairness as well. Performance and fairness, however, have mostly been studied independently. We examine characteristics of three approaches to fairness evaluation in parallel job scheduling. We examine how they represent job starvation and other aspects of discrimination. We show that the implied unfairness is not always starvation/discrimination in practice. We use simultaneous consideration of performance and fairness and compare deductions with scheduler effectiveness derived from group-wise performance evaluation. We observe that due to possible misrepresentation of starvation by fairness metrics, schedulers shown as superior may not be so in practice.