Abstract
Fairness and efficiency are two important goals of multi-resource allocation in mobile edge computing systems. We model the system as a shared computing system consisting of multiple servers, where user tasks have placement constraints and link bandwidth resources are independent of servers. For this model, we propose a mechanism, soft task share fairness - max-min efficiency (TSF-MME), to capture the trade-off between efficiency and fairness in multi-resource allocation. TSF-MME consists of \(\alpha \) task share fairness mechanism (\(\alpha \)-TSF) and max-min efficiency mechanism (MME). Compared with absolute fairness, TSF-MME can guarantee soft fairness of no less than \(\alpha \) times. The lower bound of \(\alpha \) is an adjustable value that can be set according to the fairness threshold that managers want to guarantee. Meanwhile, TSF-MME can maximize and directly display the overall efficiency of the system. Then, we design an algorithm to find the allocation of TSF-MME. Rigorous proof shows that TSF-MME satisfies soft fairness, soft sharing incentive, Pareto optimality and envy-freeness.
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This work is supported in part by the National Natural Science Foundation of China [Nos. 12071417, 61762091 and 62062065].
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Li, X., Li, W., Zhang, X. (2022). Multi-resource Allocation in Mobile Edge Computing Systems: A Trade-Off on Fairness and Efficiency. In: Cai, Z., Chen, Y., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2022. Communications in Computer and Information Science, vol 1693. Springer, Singapore. https://doi.org/10.1007/978-981-19-8152-4_18
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DOI: https://doi.org/10.1007/978-981-19-8152-4_18
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