Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Abstract

In today's increasingly popular Internet of Things (IoT) technology, its energy consumption issue is also becoming increasingly prominent. Currently, the application of Mobile Edge Computing (MEC) in IoT is becoming increasingly important, and scheduling its tasks to save energy is imperative. To address the aforementioned issues, we propose a Multi-User Multi-Server (MUMS) scheduling framework aimed at reducing the energy consumption in MEC. The framework starts with a model definition phase, detailing multi-user multi-server systems through four fundamental models: communication, offloading, energy, and delay. Then, these models are integrated to construct an energy consumption optimization model for MUMS. The final step involves utilizing the proposed L1_PSO (an enhanced version of the standard particle swarm optimization algorithm) to solve the optimization problem. Experimental results demonstrate that, compared to typical scheduling algorithms, the MUMS framework is both reasonable and feasible. Notably, the L1_PSO algorithm reduces energy consumption by 4.6 % compared to Random Assignment and by 2.3 % compared to the conventional Particle Swarm Optimization algorithm.

Keywords

• Low-power task scheduling
• Mobile edge computing
• Power consumption modeling
• Energy consumption optimization
• Particle swarm optimization