Task Scheduling Problem in Fog Computing Environment with improved Memetic algorithm

Abstract

The task scheduling problem in fog computing is one of the key challenges in the development of fog computing within next-generation communication networks. Addressing this challenge requires balancing processing performance with resource constraints while meeting network conditions. Given the distributed and heterogeneous nature, as well as the dynamic topology, optimally allocating tasks to fog nodes is a complex issue. To contribute to solving this problem, we propose a task scheduling method based on an improved Memetic algorithm. The proposed method leverages the strengths of evolutionary algorithms and local search, while incorporating a task restructuring mechanism, to enhance allocation efficiency and task processing in the fog computing environment. Simulation experiments demonstrate that the proposed method outperforms genetic algorithms, Round-Robin, Greedy methods, and the Ant Colony Optimization algorithm in terms of efficiency. This study provides a fresh, simpler approach that aligns with network conditions while still achieving the desired performance.