Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

An enriched computational platform has been unfolded with the introduction of cloud technology offering ensemble services to users that includes storage, database and processing power.More recently, the cloud technology has been upgraded to a federated environment that offers even more features where various service providers could interconnect for providing an integrated service in a transparent way to cloud users.Applications that demand enormous computing resources like for instance bioinformatics workflow applications could very well make use of the abundant cloud resources for effective execution.Fine tuning the task scheduling activity in cloud could further boost the overall cloud performance.In this paper, we had designed an optimal and ideal task scheduling algorithm that primarily focuses on reducing the cost and makespan QoS parameters, eventually leading to enhanced cloud performance.The proposed algorithm, which is a meta-heuristic enhanced hybrid version named, grey wolf optimizer cuckoo (GWOC) is formally designed from the existing grey wolf optimizer and cuckoo search algorithms.results obtained clearly justify the goal accomplishment of the proposed GWOC algorithm and its swiftness in achieving convergence, thereby clearly outperforming existing contemporaries like gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm.The proposed GWOC technique had produced an improvement of 2.11%, 3.5% and 5.17% for makespan and had reduced the cost to the tune of 7.71%, 11.3%, and 15.4% when compared with gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm respectively when used with 100 VMs.Detailed results have been presented in section 5.