Novel Approach of Workflow Scheduling with Deadline Constraint using Pareto Distribution with Hybrid Swarm Intelligence in Cloud Computing
Sahadev Upadhayay1, Pragya Gaur2
1Mr. Sahadev Upadhayay, Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut (U.P), India.
2Ms. Pragya Gaur, Assistant Professor, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut (U.P), India.
Manuscript received on 25 March 2019 | Revised Manuscript received on 02 April 2019 | Manuscript Published on 12 April 2019 | PP: 29-37 | Volume-7 Issue-6C April 2019 | Retrieval Number: F90250476C19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is a latest approach that is growing faster day by day due to its effective feature and security. Cloud computing provide a way to access the data from any place at any time. This feature makes it popular because it reduces the burden of the users. Cloud computing provides the services like infrastructure, platform and software as a service on it. Due to these feature the Size of data on cloud in increased and its effects on the efficiency of cloud. To overcome the problem like this scheduling of task on data is the best option. Workflow scheduling is a challenging task in cloud computing because user requirements and satisfaction is also considered in it, so to reduce the cost, cloud environment, has been deployed in cloud environment, resources will increase but its utilization is another challenge. To maintain & utilize resources in the cloud computing scheduling mechanism is needed. Many algorithms and protocols are used to manage the parallel jobs and resources which are used to enhance the performance of the CPU in the cloud environment. This work Particles swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for effective scheduling. . This work is based on the optimization of Total execution time and total execution cost. The results of the proposed approach are found to be effective in compare to existing methods. Intelligence optimization Particle Swarm optimization is used which is initialized by Pareto distribution. . GWO is used to converge the decision of Virtual Machine (VM) migration by its convergence to minimize cost and time as illustrated by Total execution time (TET) and Total execution cost (TEC) .It is concluded that GWO performs better in compare to existing BAT algorithm.
Keywords: Cloud Computing, Cloud Deployment Model, Resource Pooling, Fast Elasticity.
Scope of the Article: Cloud Computing