Edge Computing and IoT: Systems, Management and Security. First EAI International Conference, ICECI 2020, Virtual Event, November 6, 2020, Proceedings

Research Article

Resource Allocation Method of Edge-Side Server Based on Two Types of Virtual Machines in Cloud and Edge Collaborative Computing Architecture

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  • @INPROCEEDINGS{10.1007/978-3-030-73429-9_5,
        author={Junfeng Man and Longqian Zhao and Cheng Peng and Qianqian Li},
        title={Resource Allocation Method of Edge-Side Server Based on Two Types of Virtual Machines in Cloud and Edge Collaborative Computing Architecture},
        proceedings={Edge Computing and IoT: Systems, Management and Security. First EAI International Conference, ICECI 2020, Virtual Event, November 6, 2020, Proceedings},
        proceedings_a={ICECI},
        year={2021},
        month={7},
        keywords={Cloud and edge collaboration IO-intensive CPU-intensive Three dimensional information Resource allocation},
        doi={10.1007/978-3-030-73429-9_5}
    }
    
  • Junfeng Man
    Longqian Zhao
    Cheng Peng
    Qianqian Li
    Year: 2021
    Resource Allocation Method of Edge-Side Server Based on Two Types of Virtual Machines in Cloud and Edge Collaborative Computing Architecture
    ICECI
    Springer
    DOI: 10.1007/978-3-030-73429-9_5
Junfeng Man1, Longqian Zhao1, Cheng Peng1, Qianqian Li1
  • 1: Hunnan University of Technology

Abstract

The process of large-scale manufacturing workshops is complex, and the traditional fixed resource allocation method will cause unbalanced load. Aiming at this problem, an edge-side server resource allocation algorithm based on cloud collaborative architecture has been designed and implemented. By defining the three-dimensional information of each IO-intensive virtual machine in the compute node, the priority of the IO-intensive virtual machine is calculated. Through analyzing the relationship between the CPU-intensive virtual machine and the host physical machine, the number of CPU cores for different tasks of the CPU-intensive virtual machine is obtained, and the hardware resources are uniformly allocated in real time according to the maximum priority list. The experimental results show that the proposed algorithm can significantly satisfy the requirements of high throughput and low latency in large manufacturing workshops, and optimize the resource allocation for actual production.