Workload Analysis in a Grid Computing Environment: A Genetic Approach

Abstract

Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid is a special type of distributed system with non-interactive workloads that involve a large number of files. Partitioning of the application program/ software into a number of small groups of modules among dissimilar processors is an important parameter to determine the efficient utilization of available resources in a grid computing environment. It also enhances the computation speed. The task partitioning and task allocation activities influence the distributed program/ software properties such as IPC. This paper presents a metaheuristic model, that performs static allocation of a set of "m" modules of distributed tasks/program considering the two conflicting objectives i.e. minimizing the makespan time and balanced utilization of a set of "n" available resources of a grid computing. Experimental results using genetic algorithm indicates that the proposed algorithm achieved these two objectives as well as improve the dynamic heuristics presented in literature.

IoT sensor network data processing using the TWLGA Scheduling Algorithm and the Hadoop Cloud Platform

“…Various setups, levels of processing power, and workloads mean that certain jobs will be incorrectly allocated to nodes with high workloads. TWLGA, which takes into account the twin restrictions of time and workload, is presented as a solution to this issue and allows Job completion and logical homework [5,6]. Chromosome coding [7], node workload [8], fitness function [9], crossover, and variation [10] operations are scheduled by the TWLGA.…”