An intelligent approach of task offloading for dependent services in Mobile Edge Computing

Abstract With the growing popularity of Internet Things (IoT), Mobile Edge Computing (MEC) has emerged for reducing heavy workload at multi-cloud core network by deploying computing and storage resources edge close to users. In IoT, services are data-intensive event-driven, resulting in extensive dependencies among services. Traditional task offloading schemes face significant challenges IoT scenario with service dependencies. To this end, paper proposes an intelligent approach minimizing latency energy consumption which jointly considers scheduling resource allocation dependent MEC. Specifically, we establish system model, communication model as well performance evaluation fully considering relationships services, optimization problem is proposed delay simultaneously. Then, design a layered scheme deal dependencies, present detailed algorithms intelligently obtain optimal policies. Finally, simulation experiments carried out validate effectiveness scheme.