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

Joint Authors

Xiong, Shengwu
Attiya, Ibrahim
Abd El-aziz, M. E.

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-03-11

Country of Publication

Egypt

No. of Pages

17

Main Subjects

Biology

Abstract EN

In recent years, cloud computing technology has attracted extensive attention from both academia and industry.

The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web.

Furthermore, it promises on-demand services with new forms of the pricing package.

However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements.

To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment.

In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm.

The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit.

Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm.

The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms.

Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

American Psychological Association (APA)

Attiya, Ibrahim& Abd El-aziz, M. E.& Xiong, Shengwu. 2020. Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138744

Modern Language Association (MLA)

Attiya, Ibrahim…[et al.]. Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1138744

American Medical Association (AMA)

Attiya, Ibrahim& Abd El-aziz, M. E.& Xiong, Shengwu. Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138744

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1138744