Optimal Scheduling Method of Information System Operation and Maintenance Resources Based On Data Perception in Cloud Computing

Abstract

Through a network, cloud computing provides users with access to computer resources including software and hardware as a service which requires effective scheduling strategies to handle these resources due to the size and dynamic resource provisioning of current data centres. The difficulty in the current scheduling ways to optimise cost and execution time has led us to propose our optimization strategy based scheduling as a solution. This research proposes novel technique in information system based optimal scheduling and resource maintenance for data perception in cloud computing to improve scheduling and resource maintenance of cloud network for operation in information system through meta-heuristic glowwormkernel particle swarm optimization (MGKPSO) and Bayesian network based multi-cloud Software defined networks (SDN). The different cloud service providers selected for this experiment are: LocalStack, Google Cloud, and Amazon Web Services (AWS). The experimental analysis reveals that the proposed technique attained Quality of Service (QoS) of 73%, Throughput of 95%, latency of 67%, data transmission of 81%, Packet delivery ratio (PDR) of 93%.