Slow-movement particle swarm optimization algorithms for scheduling security-critical tasks in resource-limited mobile edge computing

Abstract Mobile edge computing (MEC) allows mobile devices to offload computation tasks to nearby MEC servers for achieving low latency and energy efficiency. This paper aims at scheduling security-critical tasks, which require data encryption and thus incur extra runtime and energy costs, in a MEC system consisting of multiple resource-limited MEC servers. The scheduling objective is to minimize task completion time as well as the mobile device’s energy consumption. We propose two slow-movement particle swarm optimization algorithms to solve the resultant NP-hard problem. Specifically, we develop a position-based mapping scheme to map particles onto scheduling solutions. The mapping method relies on the current best solution and a position-based probability model to generate high-quality solutions that can inherit the good schemata from the current best solution. To prevent the significant change in particles’ positions, we further propose a novel particle updating strategy to slow down particles’ movements, in order to explore more high-quality solutions under the guide of personal best particle and global best particle. Experimental results demonstrate that, the proposed algorithms significantly outperform the conventional particle swarm optimization algorithm in terms of both effectiveness and efficiency. Performance of the mapping method and the particle updating strategy are also investigated.