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Dynamic Multi-Resource Fair Allocation with Elastic Demands

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Abstract

In this paper, we study dynamic multi-resource maximin share fair allocation based on the elastic demands of users in a cloud computing system. In this problem, users do not stay in the computing system all the time. Users are assigned resources only if they stay in the system. To further improve the utilization of resources, the model in this paper allows users to dynamically select the method of processing tasks based on the resources allocated to each time slot. For this problem, we propose a mechanism called maximin share fairness with elastic demands (MMS-ED) in a cloud computing system. We prove theoretically that the allocation returned by the mechanism is a Lorenz-dominating allocation, that the allocation satisfies the cumulative maximin share fairness, and that the mechanism is Pareto efficiency, proportionality, and strategy-proofness. Within a specific setting, MMS-ED performs better, and it also satisfies another desirable property weighted envy-freeness. In addition, we designed an algorithm to realize this mechanism, conducted simulation experiments with Alibaba cluster traces, and we analyzed the impact from three perspectives of elastic demand and cumulative fairness. The experimental results show that the MMS-ED mechanism performs better than do the other three similar mechanisms in terms of resource utilization and user utility; moreover, the introduction of elastic demand and cumulative fairness can effectively improve resource utilization.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Thank you very much for the suggestions from the peer review, which have further improved the quality of our article and provided us with new research directions.

Funding

This work was supported in part by the National Natural Science Foundation of China [12071417] and the 14th Postgraduate Innovation Foundation of Yunnan University [No. KC-22221138].

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Authors and Affiliations

  1. School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, 650504, People’s Republic of China

    Hao Guo & Weidong Li

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  1. Hao Guo
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Contributions

Conceptualization: Weidong Li; Methodology: Hao Guo and Weidong Li; Formal analysis: Hao Guo; Original Draft: Hao Guo; Review & Editing: Hao Guo and Weidong Li.

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Correspondence to Weidong Li.

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Guo, H., Li, W. Dynamic Multi-Resource Fair Allocation with Elastic Demands. J Grid Computing 22, 35 (2024). https://doi.org/10.1007/s10723-024-09754-6

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