A Genetic Algorithm for Optimal Job Scheduling and Load Balancing in Cloud Computing

  • Authors

    • Zarina Mohamad
    • Aminu Abdulkadir Mahmoud
    • Nur Shuhadah Wan Nik
    • Mohamad Afendee Mohamed
    • Mustafa Mat Deris
    https://doi.org/10.14419/ijet.v7i3.28.23462
  • Job Scheduling, Load Balancing, Cloud Computing, Genetic Algorithm.

  • Abstract

    Cloud Computing is a new concept for pool of virtualized computer resources. There are many approaches available to improve the job scheduling and load balancing in cloud environment. However, this research focused on the Job scheduling in cloud computing environment at Virtual Machines level by considering their bandwidth and RAM size. Three (3) traditional scheduling techniques are employed (min-min, max-min, and suffrage) to find the minimum completion time possible for a given job(s) for each Virtual Machine (VM). The Genetic Algorithm (GA) is applied after the job scheduling is completed for load balancing and to attained the Quality of Service (QoS) required by properly utilizing the resources available. A CloudSim simulator is used to test the efficiency of the proposed technique. We found that the proposed technique called Random Make Genetic Optimizer (RMGO) can minimize the makespan as compared to classical job scheduling techniques.

                                                                                                                                                                                                        

     

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  • How to Cite

    Mohamad, Z., Abdulkadir Mahmoud, A., Shuhadah Wan Nik, N., Afendee Mohamed, M., & Mat Deris, M. (2018). A Genetic Algorithm for Optimal Job Scheduling and Load Balancing in Cloud Computing. International Journal of Engineering & Technology, 7(3.28), 290-294. https://doi.org/10.14419/ijet.v7i3.28.23462

    Received date: 2018-12-08

    Accepted date: 2018-12-08