LAUSR

Services are delivered promptly and efficiently managed over the Internet using cloud computing. Data and storage services are offered to users through cloud computing without regard to the actual physical location of the users. The cloud environment includes huge number of tasks and computing resources. Identification of appropriate virtual machine (VM) for allocation of resources to complete any submitted task is done through the task scheduling algorithms, which play a significant role in the cloud computing process. Task scheduling techniques improvise the makespan of the schedule and account for considerable reduction of cost expended. In this paper, an efficient hybridized scheduling algorithm that replicates the parasitic behaviour of the cuckoo and food gathering habit of the crow bird, named Cuckoo Crow Search Algorithm (CCSA) had been presented for improvising the task scheduling process. The crow bird always stares at its neighbours looking for a better food source than the one it currently possesses. At some circumstances the crow even goes a step further and steals its neighbour’s food. The CCSA had been inspired from such characteristics of these birds and had been designed to be applied in the cloud environment for identifying a suitable VM for carrying out the task scheduling process. The proposed CCSA accounts for reduction in makespan and cost and its performance had been experimentally verified by comparing it with other existing algorithms like Multi objective Ant Colony Optimization (MO-ACO), ACO, Min-Min and the results had been presented. The proposed CCSA technique had produced an improvement of 3.14%, 10.70%, and 21.60% for makespan and had reduced the overall cost to the tune of 4.56%, 11.19% and 19.35% when compared with MO-ACO, ACO, Min-Min algorithms respectively when used with 10 VMs. The detailed description of results obtained had been described in result section.