Energy-conscious scheduling in edge environments: hybridization of traditional control and DE algorithm

Abstract

Robot applications encompass a multitude of edge computing tasks, such as image processing, health monitoring, path planning, and infotainment. However, task scheduling within such environments remains a significant challenge due to the inherent limitations of edge computing resources and the dynamically fluctuating nature of workloads. EdgeCloudSim, a widely used simulation platform for edge computing, supports a conventional control strategy—Least-Loaded First-Fit Decreasing (LLFFD)—that is favored for its simplicity and speed, especially in scenarios with relatively small-scale and stable workloads. However, as the number of tasks grows and task-VM matching becomes more complex, traditional heuristics struggle to optimize resource utilization and energy consumption effectively. To address this, we propose a hybrid scheduling approach—FFDDE—that integrates the FFD heuristic with the Differential Evolution (DE) algorithm for optimized task-to-VM mapping in edge environments. Using the EdgeCloudSim simulation framework, we evaluate both strategies under diverse workload conditions, comparing their performance in terms of energy consumption and task completion time. Experimental results demonstrate that, compared with the traditional LLFFD method and the classic heuristic algorithm—GA, the hybrid DE-based strategy achieves significantly improved energy efficiency through better task consolidation. This study highlights the potential of combining fast heuristic methods with evolutionary optimization to achieve more sustainable task scheduling in edge computing scenarios.