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Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources

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Abstract

Fair and efficient allocation of multiple types of resources is a fundamental goal in cloud computing systems. Due to the heterogeneous resource requirements of a user’s jobs on CPU, memory, etc., fairness of allocation is difficult to ensure while maximizing efficiency. Existing representative multi-resource fair allocation mechanisms, such as dominant resource fairness and its follow-up work, can hardly achieve maximum efficiency while ensuring fairness. To overcome this drawback, we propose a new fair allocation mechanism, called generalized asset fairness (GAF), to maximize the system resource utilization and ensure fairness. We show that GAF satisfies many desired fairness properties. To implement our resource allocation mechanism, we design a scheduling algorithm to find feasible solutions. The experimental results show that GAF can produce a fair allocation with higher resource utilization than all previous known fair mechanisms; GAF also performs well in resource sharing.

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Data availability statement

The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China [Nos. 12071417, 61762091], the Program for Excellent Young Talents of Yunnan University, the Training Program of the National Science Fund for Distinguished Young Scholars, IRTSTYN, and the Scientific Research Foundation of Yunnan Provincial Department of Education [No. 2019J0826].

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Correspondence to Weidong Li.

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Zhang, X., Li, J., Li, G. et al. Generalized asset fairness mechanism for multi-resource fair allocation mechanism with two different types of resources. Cluster Comput 25, 3389–3403 (2022). https://doi.org/10.1007/s10586-022-03548-9

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