Mobility-aware Task Offloading and Migration Schemes in SCNs with Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) is regarded as an enabling technology to provide computing capacity closer to the mobile equipments (MEs). Due to the MEs' mobility, the execution results of offloaded tasks can't be transmitted back to MEs if they are out of the coverage of the small cell network (SCN). Therefore, the migration is needed, which causes the extra cost. In this paper, a novel mobility-aware offloading and migration scheme with MEC is proposed to maximize the total revenue of MEs. Firstly, with the analysis of MEs' mobility, the problem of maximizing the total revenue of MEs is formulated, which integrates the offloading decision and resource allocation to reduce the probability of migration. Secondly, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, so a SUb-optimal algorithm named genetic algorithm based allocation algorithm (GAAA) is proposed to solve this NP-hard problem. Finally, extensive simulations demonstrate that GAAA can effectively improve the total revenue of MEs in contrast with other baseline algorithms.