Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

Abstract

This paper proposes a fuzzy Manhattan distance-based similarity for gang formation of resources (FMDSGR) method with priority task scheduling in cloud computing. The proposed work decides which processor is to execute the current task in order to achieve efficient resource utilization and effective task scheduling. FMDSGR groups the resources into gangs which rely upon the similarity of resource characteristics in order to use the resources effectively. Then, the tasks are scheduled based on the priority in the gang of processors using gang-based priority scheduling (GPS). This reduces mainly the cost of deciding which processor is to execute the current task. Performance has been evaluated in terms of makespan, scheduling length ratio, speedup, efficiency and load balancing. CloudSim simulator is the toolkit used for simulation and for demonstrating experimental results in cloud computing environments.