Abstract
This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Multi-task Share Fairness for Efficiency-Aware Allocation (MTSFEAA), which generalizes Bottleneck-aware Allocation (BAA) to the settings of users with multiple heterogeneous tasks to run. We classify users into different groups by their dominant resources. The goals are to ensure that users in the same group receive allocations in proportion to their fair shares while users in different groups receive allocations that maximize resource utilization subject to the well-studied fairness properties such as those in DRF. Under MTSFEAA, no user (1) is worse off sharing resources than dividing resources equally among all users; (2) prefers the allocation of another user; (3) can improve their own allocation without reducing other users’ allocations. Experiments demonstrate that the proposed allocation policy performs better in terms of total number of tasks than does DRF.
Supported by the Oversea Study Program of the Guangzhou Elite Project (GEP), Also supported by the National Natural Science Foundation of China under Grant 61701181 and the Guangdong Natural Science Foundation under Grant 2017A030325430.
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Zhao, L., Du, M., Lei, W., Chen, L., Yang, L. (2018). Towards Multi-task Fair Sharing for Multi-resource Allocation in Cloud Computing. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_30
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DOI: https://doi.org/10.1007/978-3-030-00009-7_30
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