Mobility-aware computation offloading in edge computing using prediction

A key use case of edge computing is computation offloading that augments the capabilities of resource-constrained mobile devices by conserving their energy consumption and reducing latency of their applications. Edge computing resources, called cloudlets, are resource-rich computing infrastructures nearby users that aim at mitigating the overload of mobile devices and providing low-latency services. A main challenge in computation offloading to cloudlets is how to assign mobile applications to cloudlets efficiently such that the assignment captures the mobility inherent of mobile devices and leads to minimum latency during runtime of the applications. We address this problem by proposing a novel offloading approach that considers dynamics of mobile applications including mobility and changing specifications, and fully assigns applications to cloudlets, while minimizing their turnaround time (latency and execution time). We first formulate the problem as an integer programming model to minimize the turnaround time of mobile applications. This problem is an NP-hard problem. To tackle the intractability, we design a computation offloading algorithm, called OAMC, utilizing future specifications of mobile applications to obtain smart mobility-aware offloading decisions based on our prediction models. We conduct several experiments to evaluate the performance of our proposed approach. The results reveal that OAMC leads to near-optimal turnaround time in a reasonable running time.