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