Weighted quantum particle swarm task offloading optimization algorithm for time-energy minimization in mobile edge computing

Abstract

Mobile Edge Computing (MEC) is changing the computing paradigm by bringing processing resources and shifting latency to mobile network edge, which is crucial for demanding environments like IoT, augmented reality, and autonomous systems. Although progress has been made in the existing literature to tackle these challenges, many approaches fall short in optimally selecting tasks and accounting for their dependencies, which are essential for efficient offloading, leading to suboptimal energy consumption and task completion time. This work proposes Weighted quantum Particle Swarm Optimization (WQPSO), a new multi-objective algorithm for MEC task offloading, as a response to this issue. At its core, WQPSO aims to optimize energy consumption and task completion time, using an effective approach that doesn’t require extensive parameter tuning. It also provides a nearly stringent scalable framework for high-demand multi-task, multiuser, and multi-server environments. We strictly compare the Python implementation of the WQPSO algorithm with a set of state-of-the-art approaches. The findings demonstrate that WQPSO delivers an average reduction of 5.16% in task completion time and 8.66% in overall system energy consumption. These results highlight its strong potential as a highly effective solution to address the challenges in edge computing.