EAI International Conference for Research, Innovation and Development for Africa

Research Article

Job Scheduling and Resource sharing on cloud platform based on Improved Bees Algorithm.

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  • @INPROCEEDINGS{10.4108/eai.20-6-2017.2270736,
        author={Tsitsi Mubaiwa and Chiedza Hwata and Wellington Makondo and Gladman Jekese and Tendai Marengereke and Weston Govere},
        title={Job Scheduling and Resource sharing on cloud platform based on Improved Bees Algorithm.},
        proceedings={EAI International Conference for Research, Innovation and Development for Africa},
        publisher={EAI},
        proceedings_a={ACRID},
        year={2018},
        month={4},
        keywords={cloud computing software as a service scheduling multi-tenancy resource utilization},
        doi={10.4108/eai.20-6-2017.2270736}
    }
    
  • Tsitsi Mubaiwa
    Chiedza Hwata
    Wellington Makondo
    Gladman Jekese
    Tendai Marengereke
    Weston Govere
    Year: 2018
    Job Scheduling and Resource sharing on cloud platform based on Improved Bees Algorithm.
    ACRID
    EAI
    DOI: 10.4108/eai.20-6-2017.2270736
Tsitsi Mubaiwa1, Chiedza Hwata1, Wellington Makondo1, Gladman Jekese1,*, Tendai Marengereke1, Weston Govere2
  • 1: Harare Institute of Technology
  • 2: Midlands State University
*Contact email: gjekese@hit.ac.zw

Abstract

Cloud computing has been widely accepted and embraced in different fields and one offering continually developing is Software as a Service, which provides software to customers. Although economically beneficial to Cloud Service Providers, multi-tenancy poses a scheduling challenge. Allocation and reallocation of resources is the key to accommodating unpredictable demands and improving return on investment from the multitenant infrastructure. This paper proposes employment of Improved Bees Algorithm which is a modification of the Bees algorithm in scheduling of jobs as submitted by users on the cloud. The following algorithms; Round Robin, First Come First Serve, Bees and Improved Bees algorithm are executed, evaluated and finally a recommendation was made to use the Improved Bees algorithm. It is based on customer job scheduling, seeking to minimize the switching time, improve the resource utilization and the server performance.