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QoS Optimization for Mobile Ad Hoc Cloud: A Multi-agent Independent Learning Approach

In the era of ubiquitous computing, offloading certain computing tasks to the mobile ad hoc cloud (MAHC) could help mobile devices reduce execution time. However, if multiple resource demanders (RDs) offload tasks to the MAHC without a proper scheduling policy, it may cause unbalanced load distribution on each resource provider (RP), which will significantly affect the overall quality of service (QoS). To this end, in this paper, we propose a multi-agent independent learning approach aiming to optimize the QoS of MAHC. Firstly, for the totally distributed MAHC, we formulate the QoS optimization model as a non-cooperative game, where each RD will compete for maximizing its own utility. Secondly, based on the potential game theory, we rigorously prove the existence of Nash equilibrium. After that, a multi-agent independent learning algorithm is proposed to obtain the equilibrium points, and the convergence of this algorithm is analyzed accordingly. Simulation results confirm that the proposed approach could improve the balancing of load distribution and thus enhance the QoS of MAHC.