Users of cloud computing platforms pose different types of demands for multiple resources on servers (physical or virtual machines). Besides differences in their resource capacities, servers may be additionally heterogeneous in their ability to service users - certain users' tasks may only be serviced by a subset of the servers. We identify important shortcomings in existing multi-resource fair allocation mechanisms - Dominant Resource Fairness (DRF) and its follow up work - when used in such environments. We develop a new fair allocation mechanism called Per-Server Dominant-Share Fairness (PS-DSF) which we show offers all desirable sharing properties that DRF is able to offer in the case of a single 'resource pool' (i.e., if the resources of all servers were pooled together into one hypothetical server). We evaluate the performance of PS-DSF through simulations. Our evaluation shows the enhanced efficiency of PS-DSF compared to the existing allocation mechanisms. We argue how our proposed allocation mechanism is applicable in cloud computing networks and especially large scale data-centers.

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Conference 2017 IEEE International Conference on Communications, ICC 2017
Khamse-Ashari, J. (Jalal), Lambadaris, I, Kesidis, G. (George), Urgaonkar, B. (Bhuvan), & Zhao, Y. (2017). Per-Server Dominant-Share Fairness (PS-DSF): A multi-resource fair allocation mechanism for heterogeneous servers. In IEEE International Conference on Communications. doi:10.1109/ICC.2017.7996727