For effective computation offloading with multi-access edge computing (MEC), both communication and computation resources should be properly managed, considering the dynamics of mobile users such as the time-varying demands and user mobility. Most existing works regard the remote cloud server as a special edge server. However, service quality cannot be met when some of the edge servers cannot be connected. Besides, the computation capability of the cloud has not been fully exploited especially when edge servers are congested. We develop an on-line offloading decision and computational resource management algorithm with joint consideration of collaborations between device–cloud, edge–edge and edge–cloud. The objective is to minimize the total energy consumption of the system, subject to computational capability and task buffer stability constraints. Lyapunov optimization technique is used to jointly deal with the delay-energy trade-off optimization and load balancing. The optimal CPU-cycle frequencies, best transmission powers and offloading scheduling policies are jointly handled in the three-layer system. Extensive simulation results demonstrate that, with V varies in [0.1,5]×109, the proposed algorithm can save more than 50% energy and over 120% task processing time than three existing benchmark algorithms averagely.
- Edge computing
ASJC Scopus subject areas
- Computer Networks and Communications