computation offloading

Today, as the new generation of communication networks is implemented, we are witnessing a considerable change in IoT development and new programs in this context. Despite recent advancements in mobile networks and devices, the limitations of devices connected to this platform in terms of computational power and energy have resulted in sever challenges for running resource-intensive programs with exigent latency requirements. To address these challenges, the concept of computation offloading in Multi-access Edge Computing (MEC) has been recently developed, in which storage and computation resources are provided close to the user. However, due to the user mobility and changes in the profile of offloaded applications over time, the problem of assignment of edge servers to users with the aim of minimizing the overall offloading latency is a complicated task. In this regard, existing mobility-aware offloading approaches are not based on fine-grain offloading and use random and unrealistic mobility models. In this article, to address the aforementioned challenges, we propose a mobility-aware fine-grain computation offloading method to minimize the overall offloading delay. In the proposed approach, the user application is divided into several components and the offloading decision is made for each component according to the mobility and specifications of user components during the time slots defined in the system. In oner hand, this latter results in more efficient offloading decision. In the other hand, it reduces the overhead of migration since the migration of a subset of program’s components imposes lower cost compared to the migration of the entire program. Moreover, we use user profile and location prediction to optimize the offloading decisions considering the underlying context over time. According to the evaluation results, it is observed that the proposed method achieves significantly better performance compared to other alternatives while the complexity of offloading decision is kept very low.