Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing
Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques.
Citation : Younis, M., Yang, S. and Passow, B. (2017) Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing. EvoApplications 2017: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol 10199, pp. 177-189
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes