Job scheduling in the Expert Cloud based on genetic algorithms. Issue 8 (19th August 2014)

• Record Type: Journal Article
• Authors: Jafari Navimipour, Nima; Rahmani, Amir Masoud; Habibizad Navin, Ahmad; Hosseinzadeh, Mehdi
• Editors: Ramage, Magnus; Ramage, Magnus
• Is Part Of: Kybernetes. Volume 43:Issue 8(2014)
• Publication Date: 2014-08-19
• Subjects: Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.505
• DOI: 10.1108/K-02-2013-0018
• Languages: English
• ISSNs: 0368-492X

Abstract

Abstract : Purpose: Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people by employing Internet infrastructures and Cloud computing concepts without any information of their location. Job scheduling is one of the most important issue in Expert Cloud and impacts on its efficiency and customer satisfaction. In this paper we propose an applicable method based on genetic algorithm for job scheduling in Expert Cloud. Design/methodology/approach: Because of the nature of the scheduling issue as a NP-Hard problem and the success of genetic algorithm in optimization and NP-Hard problems, we used a genetic algorithm to schedule the jobs on human resources in Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on response time; one point crossover and swap mutation are also used. Findings: The results indicate that the proposed method can schedule the received jobs in appropriate time with high accuracy in comparison to common methods (FCFS, SPN and HRRN). Also the proposed method has better performance in term of total execution time, service+wait time, failure rate and HR utilization rate in comparison to common methods. Originality/value: In this paper the job scheduling issue in Expert Cloud is pointed out and the approach to resolve the problem is applied into a practical example.