Energy-Efficient Computation Offloading for Static and Dynamic Applications in Hybrid Mobile Edge Cloud System

As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, named Particle swarm optimization based on Genetic Learning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs' association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.