A Scheduling Strategy for Reduced Power Consumption in Mobile Edge Computing

Abstract

The emergence of cloud computing has promoted the explosive growth of applications, however, with the repaid generation of an unprecedented volume and variety of data, the demand for high-quality mobile services with low latency has been increasing. Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some realtime requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. At present, the industry has two major problems for edge computing. One is to reduce the delay for the tasks. The other one is to consider the endurance of power consumption. In this paper, we focus on saving the power consumption of the system to provide an efficient scheduling strategy in mobile edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reducing the power consumption and guarantees the user's delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm.