Stochastic Programming Methods for Workload Assignment in an Ad Hoc Mobile Cloud

Abstract

In order to achieve better system performance, the concept of an ad-hoc mobile cloud, whereby a mobile device can access resources such as processing, data or storage at other neighbouring nodes, has been proposed. The difficulty that arises with this concept is the mobility of nearby devices, i.e., a neighboring device may move out of range before it can communicate its results back to the source device. In this paper, we propose a workload assignment scheme between a source device and nearby mobile devices that takes into account the randomness of the connection time between these devices. In order to cope with this randomness, we adopt a multi-stage stochastic programming approach which is able to take posterior recourse actions to compensate for inaccurate predictions. Moreover, in order to motivate the available mobile devices to cooperate, we formulate a distributed multi-stage stochastic buyer-seller game (MSSBSG) in which different mobile devices attempt to maximize their utilities. Our results show that the stochastic programming approach outperforms several baseline schemes and the MSSBSG approach effectively promotes cooperation between mobile devices and achieves the best overall performance compared to simpler approaches that do not take stochastic operating conditions into account.