Abstract
The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform. The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer. It also derives an objective function with candidate solution (schedule) as input and the outcome is the makespan value denoting the quality of the candidate solution. The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.
Highlights
Grid computing is a set of heterogeneous and dynamic resources from various administrative domains and provides access to the resource [1]
The IGSS-deer hunting optimization algorithm (DHOA) algorithm has derived an objective function with the minimization of makespan in the grid environment with the candidate solution as input and makespan value as output
It derives an objective function with candidate solution as input and the outcome is the makespan value denoting the quality of the candidate solution
Summary
Grid computing is a set of heterogeneous and dynamic resources from various administrative domains and provides access to the resource [1]. The primary objective of this technique is to distribute scattered and idle resources like storage capacity and computation power. The major purpose of grid computing is problem solving, i.e., time consuming and complex [2]. This aim might be attained by the processing power of the computer present on the grid platform. Computation grid is determined as integration of software and hardware infrastructures which provide inexpensive, consistent, and pervasive accessing to higher end computation resources. Data grid is an integration of huge datasets i.e., primarily utilized for providing data to the application [3]. To exploit the resource effectively and in order to fulfill each requirement of the user, it is necessary to efficient scheduling approach
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