Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

Mobile device cloud (MDC) is a collaborative cloud computing platform over which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device for running compute-intensive applications. A major challenge of such a platform is maximizing user quality-of-experience (QoE) at minimum cost while providing attractive incentives to workers' mobile devices. In state-of-the-art works, either a voluntary task execution or merely resource-cost driven mechanism has been applied to minimize the task execution time while overlooking payment of any additional incentive to the worker devices for their quality services. In this paper, we develop a computational framework for MDC where the afore-mentioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Due to the NP-hardness of MOLP, we offer two greedy worker selection algorithms for maximizing user QoE or minimizing execution cost. In both algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs of desirable properties of the proposed incentive mechanisms are presented. Simulation results illustrate the effectiveness of our incentive algorithms compared to the state-of-the-art approaches.

Automated summary

Mobile device cloud (MDC) is a collaborative cloud computing platform where nearby smart devices form an alliance to share resources, aiming to mitigate resource scarcity of individual user devices. The major challenge is to maximize user quality-of-experience (QoE) at minimum cost while attracting mobile device workers with incentives. This paper presents a computational framework with a multi-objective linear programming optimization function and offers two greedy worker selection algorithms to enhance user QoE or reduce execution cost by providing incentives based on the QoE offered. Simulation results demonstrate the effectiveness of the proposed incentive algorithms compared to existing approaches.