I-MaxStd: Toward Improving MaxStd Task Scheduling Technique for Cloud Computing Environment

Abstract

Job scheduling process under the roof of Cloud is consisting of three phases: Resource Discovery, Resource Selection and Task Scheduling (Yousif, A., et al., 2011. A taxonomy of grid resource selection mechanisms. International Journal of Grid and Distributed Computing, 4(3), pp.107.117). Among them, task scheduling is always treated a cumbersome activity because it mapped task(s) to their assigned resource(s) based on various constraints and impositions according to requirements. Task scheduling in Cloud environment is broadly categorized into two streams that are: Heuristic and Meta-Heuristic (sometimes combination of both). Heuristic approach is further categorized into two streams: Immediate mode or online approach and Batch mode or offline scheduling technique. MaxStd, heuristic mapping, is one of the efficient batch modes scheduling technique for independent task(s) due to its inherent efficiency and performance. In this paper, we have proposed an improved version of MaxStd (I-MaxStd) that refines the mapping process of conventional MaxStd to yields an efficient output in the form of reduced makespan and better resource average utilization rate without compromising its legacy. The validation of proposed work has been done for heterogeneous types of ETC matrices being used as dataset.