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In recent years, edge computing, as an important pillar for future networks,has been developed rapidly. Task offloading is a key part of edge computingthat can provide computing resources for resource-constrained devices to runcomputing-intensive applications, accelerate computing speed and save energy.An efficient and feasible task offloading scheme can not only greatly improvethe quality of experience (QoE) but also provide strong support and assistancefor 5G/B5G networks, the industrial Internet of Things (IIoT), computingnetworks and so on. To achieve these goals, this paper proposes an adaptiveedge task offloading scheme assisted by service deployment (SD-AETO) focusingon the optimization of the energy utilization ratio (EUR) and the processinglatency. In the pre-implementation stage of the SD-AETO scheme, a servicedeployment scheme is invoked to assist with task offloading considering eachservice's popularity. The optimal service deployment scheme is obtained byusing the approximate deployment graph (AD-graph). Furthermore, a taskscheduling and queue offloading design procedure is proposed to complete theSD-AETO scheme based on the task priority. The task priority is generated bythe corresponding service popularity and task offloading direction. Finally, weanalyze our SD-AETO scheme and compare it with related approaches, and theresults show that our scheme has a higher edge offloading rate and lowerresource consumption for massive task scenarios in the edge network.

SD-AETO: Service Deployment Enabled Adaptive Edge Task Offloading in MEC