Cost Optimization for Partial Computation Offloading and Resource Allocation in Heterogeneous Mobile Edge Computing

Mobile edge computing (MEC) is an emerging architecture that supports computing, storage, and networking resources to users' mobile devices (MDs). MDs have limited resources and energy capacity, and they have to offload some tasks of computational/delay-intensive applications to their nearby small base station (SBS), which is a paradigm of MEC. Although task offloading decreases energy consumed by MDs, it brings additional transmission delay among MDs and SBS/cloud data center (CDC), and processing delay in SBS and CDC. It is challenging to minimize the total cost of a heterogeneous system including MDs, an SBS and a CDC while strictly guaranteeing delay limits of tasks. This work adopts different queuing systems to analyze the performance of MDs, the SBS and the CDC in the heterogeneous MEC system. Then, this work formulates a constrained optimization problem to minimize the total cost of the system. To solve it, we design a hybrid method named Genetic Simulated-annealing-based Particle swarm optimization (GSP) integrating genetic operations of genetic algorithm and the Metropolis acceptance rule of simulated annealing into particle swarm optimization. Real-life data-based simulation results demonstrate that GSP's solution outperforms several state-of-the-art algorithms in terms of total cost.