Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers
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.
Cite
@article{arxiv.1308.0083,
title = {Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers},
author = {Wei Wang and Baochun Li and Ben Liang},
journal= {arXiv preprint arXiv:1308.0083},
year = {2013}
}