An Efficient Hybridization Algorithm Based Task Scheduling in Cloud Environment
P. Neelima, A. Rama Mohan Reddy · Journal of Circuits Systems and Computers · 2017
The present paper describes a hybrid self-adaptive learning global search algorithm and firefly algorithm (HSLGSAFA)-based model for task scheduling in cloud computing. The proposed hybrid model combines gravitational search algorithm (GSA), which has been successfully scheduling the task in the application, with the use of SL strategy and the FA. The basic scheme of our approach is to utilize the benefits of both SLGSA algorithm and firefly algorithm and not including their disadvantages. In HSLGSAFA, each dimension of a solution represents a task and a solution as a whole signifies all tasks’ priorities. The vital issue is how to allocate users’ tasks to exploit the income of Infrastructure as a Service (IaaS) provider while promising Quality-of-Service (QoS). The generated solution is proficient to assure user-level QoS and improve IaaS providers’ credibility and economic benefit. The HSLGSAFA method also used to design the hybridization process and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.