Resource selection in computational grids based on learning automata

• Published in: Expert systems with applications 2019-07, Vol.125, p.369-377
• Main Authors: Enami, Alireza, Akbari Torkestani, Javad, Karimi, Abbas
• Format: Article
• Language: English
• Subjects: Algorithms; Automata theory; Computational grids; Computer simulation; Dynamic programming; Grid computing; Heuristic methods; Heuristic resource scheduling; Learning automata; Machine learning; Resource management; Resource selection; Response time; Schedules; Task scheduling
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.01.076

•Learning automata-variable actions is used to solve the resource selection problem.•The proposed algorithm based on job predicted time.•This algorithm based on the list of resources that discovered in discovery stage.•Two different implementations of the algorithm are performed.•Proposed algorithm outperforms in terms of average response time and throughput. Grid computing, in simplest terms, is distributed computations that have reached higher evolution level. Grid scheduler is part of Grid. Scheduler generates an assignment of jobs to resources using the resource information provided by the Grid information service. Since the problems raised in the resource management system are NP-hard, classical methods, such as dynamic programming, are useful only for small-size problems. Being capable of producing efficient schedules at an acceptable time, even for large samples of the problem, heuristic algorithms are promising methods for solving the scheduling problem. The resource scheduling process in the Grid consists of three main phases: resource discovery, resource selection and job execution. In this paper, we propose an algorithm based on learning automata to resource selection in computational Grid. In this algorithm, decisions are made based on the list of resources that discovered at the resource discovery phase, and after being selected based on predicted time to execution or completion job, they would sent to the next phase namely the execution phase. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with several existing methods in terms of the average turn-around time, average response time and throughput.