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Multi-resource Fair Sharing for Multiclass Workflows by Jian Tan; Li Zhang; Min Li; Yandong Wang is a Computer Science article available to read on EtoBox.

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Multi-resource sharing for concurrent workows necessitates a fairness criteria to allocate multiple resources to work-flows with heterogeneous demands. Recently, this problem has attracted increasing attention and has been investigated by assuming that each workow has a single class of jobs and that each class contains jobs of the same demand profile. The demand profile of a class represents the required multi-resources of a job. However, for typical applications in cloud computing and distributed data processing systems, a workow usually needs to process multiple classes of jobs. Relying on the concept of slowdown, we characterize fairness for multi-resource sharing and address scheduling for multiclass workows. We optimize the mixture of different classes of jobs for a workow as optimal operation points to achieve the least slowdown, and discuss desirable properties for these operation points. These studies assume that the jobs are infinitely divisible. When jobs are non-preemptive and indivisible, any fairness criteria that only relies on the instantaneous resource allocation cannot be strictly maintained at every time point. To this end, we relax the instantaneous fairness to a

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Author

Jian Tan; Li Zhang; Min Li; Yandong Wang

Publisher

ACM

Published

2015

Language

EN

Field

Computer Science (Physical Sciences)