Abstract
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.
Highlights
With the development of mobile communication technology and the popularization of smart mobile devices, such as smart phones and smart watches, various network services and applications are constantly emerging [1,2]
In order to get a better solution, this paper proposes an optimal integer linear programming (OILP) algorithm, which is performed in two steps
Because the OILP algorithm considers the task priority, priority, it will prioritize high-priority tasks at the beginning, and take energy supplement as a it will prioritize high-priority tasks at the beginning, and take energy supplement as a secondary secondary consideration, which will lead to a longer time for the battery energy to reach stability, as consideration, which will lead to a longer time for the battery energy to reach stability, as shown in shown in Figure 2a, when the number of the local device N = 6, the number of the mobile edge computing (MEC) server M =
Summary
With the development of mobile communication technology and the popularization of smart mobile devices, such as smart phones and smart watches, various network services and applications are constantly emerging [1,2]. Owing to the development of 5G technology, users can offload tasks to the MEC server for processing through wireless and cellular networks This can reduce the processing delay from the local to the cloud server; in addition, it can improve the task processing capacity and extend the life of the local devices. MEC server, and edge cloud server collaborative processing task system, reasonably allocate energy and computing resources, and improve system performance, e.g., task processing capacity; Use Lyapunov stability theory for local mobile device power stability and task processing delay as optimization goals, and jointly consider the local mobile device’s energy collection, computing power allocation, wireless link transmit power allocation, and MEC–edge cloud server resource allocation problem.
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