English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC–edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU’s utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.

Resource Allocation Strategy of Edge Systems Based on Task Priority and an Optimal Integer Linear Programming Algorithm