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.

  • 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.

                                                                                                                                                                                                        

     

  • References

    1. [1] Google. www.google.com/trends.

      [2] Erkoc M F, Kert S B. Cloud computing for distributed university campus: A prototype suggestion. Proceedings of the International Conference the Future of Education, 2014, pp. 1-5.

      [3] Srichandana S, Kumar T A, Bibhudatta S. Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm. Future Computing and Informatics Journal, 2018, 1-21, 2018.

      [4] Bittencourt L F, Goldman A, Madeira E R M, Fonseca N L S, Sakellariou R. Scheduling in distributed systems: A cloud computing perspective. Computer Science Review, 30, 31-54, 2018.

      [5] Huth A, Cebula J. The basics of cloud computing. United States Computer, 2011.

      [6] Keshanchia B, Souria A, Navimipour N J. An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing. Journal of Systems and Software, 124, 1-21, 2017.

      [7] Goyal T, Agrawal A. Host scheduling algorithm using genetic algorithm in cloud environment. International Journal of Research in Engineering and Technology, 1(1), 7-12, 2013.

      [8] Gu J, Hu J, Zhao T, Sun G. A new resource scheduling strategy based on genetic algorithm cloud computing environment. Journal of Computers, 7(1), 42–52, 2012.

      [9] Katyal M, Mishra A. Application of selective algorithm for effective resource provisioning in cloud computing environment. International Journal on Cloud Computing: Services and Architecture, 4(1), 1-10, 2014.

      [10] Agarwal A, Jain S. Efficient optimal algorithm of task scheduling in cloud computing environment. International Journal of Computer Trends and Technology, 9(7), 344-349, 2014.

      [11] Huang C, Guan C, Chen H, Wang Y, Chang S, Li C, Wang C. An adaptive resource management scheme in cloud computing. Engineering Applications of Artificial Intelligence, 26, 382-389, 2013.

      [12] Rawat S S, Bindal U. Effective load balancing in cloud computing using genetic algorithm. International Journal of Computer Science, Engineering and Information Technology Research, 3(4), 91-98, 2013.

      [13] Dasgupta K, Mandal B, Dutta P, Mondal J K, Dam S. A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technology, 10, 340-347, 2013.

      [14] Khan D H, Kapgate D, Prasad P S. A review on virtual machine management techniques and scheduling in cloud computing. International Journal of Advanced Research in Computer Science and Software Engineering, 3(12), 838-845, 2013.

      [15] Kruekaew B, Kimpan W. Virtual machine scheduling management on cloud computing using artificial bee colony. Proceeding of the International Multi-Conference of Engineers and Computer Scientists, 2014, pp. 12-14.

      [16] Kaur S, Verma A. An efficient approach to genetic algorithm for task scheduling in cloud computing environment. International Journal of Information Technology and Computer Science, 10, 74-79, 2012.

  • Downloads

  • 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