Hybridization of algorithms for Cloud Computing

Cloud is a term, which involves virtualization, distributed computing, networking, software and web services. A cloud consists of several elements such as clients, data center and distributed servers. It consists of various advantages like fault tolerance, high availability, scalability, flexibility, reduced overhead for users by reducing the cost of ownership, on demand services etc. Cloud computing can be described as a model of Internet- based computing due to Internet based development and utilization of computer technology. Scheduling is a critical problem in Cloud computing, because a cloud service provider has to serve many users in Cloud Computing System. So job scheduling is the main issue in establishing Cloud Computing Systems. The main goal of scheduling is to maximize the resource utilization, to reduce waiting time, execution time. In this thesis, an efficient Hybrid scheduling approach has been proposed in computational cloud. Proposed work is grouping the tasks before resource allocation according to job priority to reduce the communication overhead. Here tasks are grouped together based on the chosen resources characteristics, to maximize resource utilization and minimize processing time. Hence in this thesis, we have specifically focused on improving computational cloud performance in terms of CPU utilization time, Executed task and Response time. A simulation of proposed algorithm is conducted on real time cloud server. Experimental results show that proposed hybrid algorithm performs better than FCFS and Priority