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Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm.

Ibrahim Attiya1,2, Mohamed Abd Elaziz2,3, Shengwu Xiong1

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This summary is machine-generated.

This study introduces a new cloud job scheduling algorithm, HHOSA, combining Harris Hawks Optimization and Simulated Annealing. HHOSA significantly reduces job completion time and improves efficiency for large-scale cloud computing tasks.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Cloud computing offers on-demand IT services but faces NP-complete job scheduling challenges.
  • Resource dynamicity and varying application needs complicate cloud job scheduling.

Purpose of the Study:

  • To develop an efficient job scheduling algorithm for cloud environments.
  • To address the complexities of resource dynamicity and on-demand requirements in cloud job scheduling.

Main Methods:

  • A modified Harris Hawks Optimization (HHO) algorithm integrated with Simulated Annealing (SA) was proposed, termed HHOSA.
  • The HHOSA algorithm was implemented and evaluated using the CloudSim toolkit.
  • Performance was assessed using both standard and synthetic workloads against existing algorithms.

Main Results:

  • HHOSA demonstrated significant reductions in makespan (job completion time) compared to standard HHO and other state-of-the-art algorithms.
  • The proposed algorithm exhibited faster convergence rates, especially in larger search spaces.
  • HHOSA proved effective for large-scale cloud scheduling problems.

Conclusions:

  • The HHOSA algorithm offers a superior approach to cloud job scheduling, enhancing solution quality and convergence speed.
  • This method is well-suited for optimizing large-scale cloud computing environments.
  • The integration of SA with HHO effectively addresses the limitations of existing scheduling algorithms.