Abstract
•Proposed MRH dynamically increases users' satisfactions.•MRH improves replica selection in data grids.•MRH incorporates quality of service parameters in replica selection decision making.•Criteria for evaluating MRH stated.•MRH results show better performance in comparison to previously used methods.
Replica selection in data grids aims to select the best replica location based on the quality-of-service parameters preferred by the user. This choice is important because of the limited number of available data resources in comparison with the large number of users. Typically, user requests are fulfilled in a first-in, first-out manner. This may satisfy the users at the beginning of the queue more than those at the end. Better results can be achieved by considering the requests of all users simultaneously, thereby leading to a higher level of overall satisfaction; however, this is a difficult task because it requires a vast order of magnitude to search through a huge set of users. Therefore, in this study, the proposed combination of hybrid of the genetic algorithm and user-preference algorithm is used to overcome this problem. The results overwhelmingly verify that the proposed hybrid approach outperformed previously known used methods significantly.