A Stochastic Workload Distribution Approach for an Ad Hoc Mobile Cloud

Mobile devices like smartphones have become the computing device of choice for many users, heralding the era of mobile computing. Many applications have been developed to run on mobile devices. However, despite the increased processing and wireless network speeds of mobile devices, their resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular computationally intensive ones such as multimedia processing, often require more resources than a mobile device can afford. To overcome this hurdle, we propose a mobile ad-hoc cloud in which a mobile device can access resources from other sources, such as nearby mobile devices, to share the workload. The difficulty that arises with this concept is the mobility of nearby devices, i.e. A neighbouring device may move out of range before it can communicate its results back to the source node. In this paper, we propose a workload distribution scheme among these nearby mobile devices that takes into account the randomness of the connection time between cooperating 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. Numerical studies and simulations were carried out to evaluate the performance of this scheme. The results show that the stochastic programming approach outperforms a naive scheme and a baseline scheme that only considers the average connection time.