COSTA: Cost-aware Service Caching and Task Offloading Assignment in Mobile-Edge Computing

This paper considers a Mobile-Edge Computing (MEC) enabled wireless network where the MEC-enabled Base Station (MBSs) can host application services and execute computation tasks corresponding to these services when they are offloaded from resource-constrained mobile users. We aim at addressing the joint problem of service caching—the provisioning of application services and their related libraries/database at the MBSs—and task-offloading assignment in a densely-deployed network where each user can exploit the degrees of freedom in offloading different portions of its computation task to multiple nearby MBSs. Firstly, an offloading cost model is introduced to capture the user energy consumption, the service caching cost, and the cloud usage cost. The underlying problem is then formulated as a Mixed-Integer Linear Programming (MILP) problem, which is shown to be NP-hard. Given the intractability of the problem, we exploit local-search techniques to design a polynomial-time iterative algorithm, named COSTA. We prove that COSTA produces a locally optimal solution with cost of at most a constant approximation ratio compared to the optimum. Trace-driven simulations using the workload records from a Google cluster show that COSTA can significantly reduce the offloading cost over competing schemes while achieving a very small optimality gap.