Hybrid-based novel approach for resource scheduling using MCFCM and PSO in cloud computing environment

Cloud computing is a growing environment. Many of the users are interested to outsource their data in cloud; however, load balancing in cloud is still at risk. Resource allocation plays a major role in load balancing. In this scheduling problem, independent task in cloud computing can allocate resource by the summary of modified canopy fuzzy c-means algorithm (MCFCMA). To allocate task to their corresponding resource, particle swarm-based optimization algorithm (PSO) is used. In proposed scheme, first independent task selected based on load feed-back, cluster the requested task using MCFCMA and schedule task to each virtual machine. VM selects parallel execution in virtual machine manager. Calculate feature value using PSO algorithm. Allocate resource to the task. Since our proposed system selects resource based on parallel execution, it reduces load balancing in Cloud Quantum Computation (CQC). The proposed system overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.

Automated summary

Cloud computing faces risks in load balancing, but these can be overcome through the use of modified canopy fuzzy c-means algorithm and particle swarm-based optimization algorithm for resource allocation and task scheduling.