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

Abstract

Mobile Device Cloud (MDC) is a collaborative mobile cloud computing platform in which neighboring smart devices form an alliance of shared resources to mitigate resource-scarcity of an individual user device. It unfolds an improved computing opportunity for hand-held mobile devices to run compute-intensive applications like visual text translation, face recognition, augmented reality, and real-time health monitoring etc. exploiting code offloading mechanism. However, the sustainability of such a distributed platform depends on spontaneous participation of the in- volved mobile devices, i.e., resource-requester (buyer) and resource-provider (seller or worker). A fundamental challenge in such a resource-trading system is the se- lection of reliable worker mobile devices that enhances the computation quality of user applications. Moreover, participation of the worker mobile devices greatly depends on their compensations provided for the used resources. In this thesis, we focus on incentivizing mobile worker devices based on their task execution qualities to materialize a sustainable MDC system. Selection of worker mobile devices for task offloading imposes great research challenges including computation quality and worker reliability. Unfortunately, these two performance parameters often oppose each other. In this thesis, we rst develop an optimization framework that trades-off in between application execution speedup and reliability while maintaining device energy within a prede ned range. We also design an algorithm for developing a dependency tree among the modules of a software application so as to allow higher number of parallel executions, wherever and whenever it is possible. The emulation results of the proposed algorithm outperform the relevant state-of-the-art works in terms of application completion time, communication latency and rescheduling overhead. The second contribution of this thesis is to maximize user Quality-of-Experience (QoE) at minimum cost while providing attractive incentives to mobile worker devices. In literature works, mobile devices are assumed either to take part in execution voluntarily or aim to optimize one objective parameter (quality or cost) only. In this thesis, the aforementioned challenging problem is formulated as a multi-objective linear programming (MOLP) optimization function that exploits reverse-auction bidding policy. Practical application scenarios have been considered to trade-off between the cost and quality of execution. Due to NP-hardness of the MOLP, we offer two greedy worker selection algorithms for maximizing user QoE and minimizing execution cost. In both the algorithms, the amount of incentive awarded to a worker is determined following the QoE offered to a user. Theoretical proofs on holding desirable properties of the proposed incentive mechanisms have been presented. Simulation results depict effectiveness of our incentive algorithms compared to the state-of-the-art approaches.