Fair multi-resource allocation in mobile edge computing with multiple access points
Erfan Meskar, Ben Liang · 2020
We consider the problem of fair multi-resource allocation for mobile edge computing (MEC) with multiple access points. In MEC, user tasks are uploaded over wireless communication channels to the access points, where they are then processed with multiple types of computing resources. What distinguishes fair multi-resource allocation in the MEC environment from more general cloud computing is that a user may experience different levels of wireless channel quality on different access points, so that the user's channel bandwidth demand is not fixed. Existing resource allocation studies for cloud computing generally consider Pareto Optimality (PO), Envy-Freeness (EF), Sharing Incentive (SI), and Strategy-Proofness (SP) as the most desirable fairness properties. In this work, we show these properties are no longer compatible in MEC, since there exists no resource allocation rule that can satisfy PO+EF+SP or PO+SI+SP. Hence, we propose a resource allocation rule, called Maximum Task Product (MTP), that retains PO, EF, and SI. Extensive simulation driven by Google cluster traces further shows that MTP improves resource utilization while achieving these fairness properties.