Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm
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View abstract on PubMed
Summary
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.
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.
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