2020 VOL.3 Feb No.1

• Title: Research on cloud computing resource scheduling method based on improved quantum particle swarm optimization
• Name: Merlin Ralap
• Company: University of Northern British Columbia
• Abstract:

  In order to effectively reduce the overall operating load during resource scheduling under cloud computing, the traditional quantum particle swarm optimization algorithm is improved, and a new cloud computing resource scheduling method is proposed based on the improved quantum particle swarm algorithm. Applying new principles and mechanisms, re-determining the quantum speed, improving the quantum particle swarm algorithm, and establishing the current resource deployment network topology through the link layer discovery protocol, perceive management of core resources, measure the current network status in real time, and obtain specific information about the global topology, network bandwidth, and transmission delay of the fiber optic network. Based on the KSP network cloud computing algorithm, based on the original address and destination address of the obtained information packet, According to the design evaluation criteria, calculate the different paths that can be used when data resources are deployed and distributed; build a link state evaluation module to comprehensively evaluate and prioritize all paths. By designing the threshold in advance, the data resources that need to be allocated will be diverted according to the path priority to ensure the big data load balance. Experimental data shows that after applying the designed resource allocation algorithm, the operating load is reduced by 29% when resources are input, and the operating load is reduced by 32% when resources are output, which can prove that this method can effectively reduce the resource allocation load.

• Keyword: Quantum Particle Swarm; Cloud Computing; Resource Scheduling;
• DOI: 10.12250/jpciams2020010128
• Citation form: Merlin Ralap.Research on cloud computing resource scheduling method based on improved quantum particle swarm optimization[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 53-59.

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