Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers

Wei Wang; Baochun Li; Ben Liang

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arXiv · 1308.0083

Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers

Wei Wang·Baochun Li·Ben Liang

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Abstract

We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no user has an incentive to lie about its resource demand. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.

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BibTeX RIS

Wei Wang, Baochun Li, Ben Liang. 2013-08-01. Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers. https://arxiv.org/abs/1308.0083

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