Live Prefetching for Mobile Computation Offloading
Abstract: Mobile computation offloading refers to techniques for offloading computation intensive tasks from mobile devices to the cloud so as to lengthen the formers' battery lives and enrich their features. The conventional designs fetch (transfer) user-specific data ata from mobiles to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio radio-access access networks. To solve this problem, the novel tech technique nique of live prefetching, which seamlessly integrates the task-level level computation prediction and prefetching within the cloudcloud computing process of a large program with numerous tasks, is proposed in this paper. The technique avoids excessive fetching but rretains etains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile mob energy consumption under a deadline constraint. The policies enable real-time real control of the prefetched--data data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based threshold structure, selecting candidate andidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design closeclose to-optimal optimal prefetching policies to fast fading channels. Compared with fetching