A Resource Scheduling with Load Balancing in Cloud Environment Using Particle Swarm Optimization
Cloud computing is a perfect platform for executing complex applications in any network. Dynamic resource scheduling for multi-objective schedules has an economic background process for fluctuating workloads. Execution assessment of Cloud Computing foundations is required to anticipate and evaluate the money saving advantage of a procedure portfolio and the relating Quality of Service (QoS) experienced by clients. Thus, focusing on Load balancing in the cloud computing environment has a significant impact on the performance. This paper takes care of the multi-objective resource provisioning scheme for handling multiple task classes for various workload facility. In this proposal, the project is using the Best Partition Searching for distributing a file system to another cloud environment. In this data's are split based on domain and stored in cloud storage. From this, the data's are shown based on the field which user search. We propose a particle swarm optimization is an artificial intelligence to perform continues workload scheduling for various attributes. So that balancing of cloud (main server) is reduced with low of cost while the cloud user gets the file from the cloud server and then User also getting an efficient and improves user satisfaction using our proposed method. These are implemented using particle swarm optimization for allocating a user downloading a file in the cloud environment. The proposed plan has produced more scheduling performance and low time complexity.
Author Name: R. Sandhiya and D. Radhika
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Keywords: Cloud Computing, Best Partition Searching for Distributing a File System, Particle Swarm Optimization, and Workload Scheduling