Hybrid Form of Swarm and Forging Algorithm for Task Scheduling in Cloud Computing Environment

  • Anjani Rai et al.


Cloud computing needs diverse requirements being placed on the periodically varying resources. Task scheduling has a significant role to play in cloud computing scenarios, and this process of scheduling requires scheduling the tasks to the virtual machine when reducing the makespan and expenditure involved. The problem of task scheduling is classified under NP hard. An effective scheduling technique improves the cloud computing services to become better and quicker. Quality of Service (QoS) is highly dependent on how better the scheduling of the services is done on the resources available. In the earlier techniques, various combinations of QoS parameter(s) are taken into consideration for the optimization of task scheduling algorithms. Parameters including cost and time taken for execution, budget, and deadline are focused here. For this purpose, generally, optimization algorithms are utilized for solving the scheduling problems faced in the cloud.Hence for task scheduling a Swarm optimization approach referred as Particle swarm optimization (PSO) and Bacterial forging Algorithm (BFO) known as HPSBFO is proposed efficient task schedulingin cloud computing. Two individual Swarms are brought into use and each one has a specific aim formulated in such a manner that the info obtained from one of the swarms is utilized for updating the other swarm’s velocity. In this manner, both the swarms collaborate with one another to obtain solution set that is efficient. On comparison with the fundamental PSO that uses weighted sum technique, this approach is confirmed to yield superior results in terms of time taken for execution, rate of defect and throughput.

How to Cite
et al., A. R. (2019). Hybrid Form of Swarm and Forging Algorithm for Task Scheduling in Cloud Computing Environment. International Journal of Control and Automation, 12(6), 57 - 68. Retrieved from http://sersc.org/journals/index.php/IJCA/article/view/2019