Evolutionary Computation Algorithms for Task Scheduling in Distributed Computing

Numerous metaheuristic algorithms have been used to optimize resource allocation challenges in heterogeneous distributed computing systems, such as heterogeneous cloud environments. One such challenge is assigning tasks to available resources based on specific criteria to reduce execution time, improve throughput, and enhance system performance. The research revealed that the majority of studies were based on Computational Intelligence algorithms inspired by natural systems. In this paper, four algorithms from the Evolutionary Algorithms group are evaluated under different loads to provide a comprehensive insight into their performance by applying simulation-based optimization as a research method. The experimental results of our implementation of the Genetic Algorithm, Differential Evolution, Evolution Strategies and Evolutionary Programming are compared considering several performance metrics. Overall results demonstrate that the Evolution Strategies algorithm exhibits the best performance. Potential future research directions and associated challenges are discussed as well.