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Analyzing Task Scheduling Algorithms in Cloud Computing for Optimal Performance

Authors: Pramod Kumar Arya

Published: 2025-03-05 23:43:52.810

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Abstract

Cloud computing has gained significant traction due to its ability to provide scalable, on-demand resources, facilitating efficient data processing and management. A crucial component of cloud performance is task scheduling, which determines how tasks are allocated to remote servers for execution. This paper focuses on analyzing and implementing three widely used task scheduling algorithms: First-Come, First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR). The study aims to evaluate how these algorithms influence cloud system performance, particularly in terms of task execution efficiency, resource utilization, and system throughput. By simulating various cloud environments and workload scenarios, the paper assesses each algorithm's strengths and limitations. The analysis highlights how the selection of scheduling algorithms directly impacts cloud performance, emphasizing the need for optimized task allocation strategies to ensure better system resource management. The findings demonstrate that while each algorithm has its advantages in specific contexts, effective scheduling is crucial for maintaining overall system stability and maximizing resource utilization. The paper concludes with recommendations for selecting the most appropriate algorithm based on workload characteristics and desired performance outcomes, offering valuable insights into improving cloud computing efficiency.

Keywords

Cloud computing, Task scheduling, FCFS, SJF, Round Robin, Resource utilization.

References

  1. Abdulkadir, A., & Muda, M. (2014). Task Scheduling Algorithms in Cloud Computing: A Survey. International Journal of Computer Science and Information Security (IJCSIS), 12(10), 34-40.
  2. Alam, M. S., & Khan, M. A. (2015). A Survey on Task Scheduling Algorithms in Cloud Computing: Issues, Challenges, and Future Directions. International Journal of Computer Applications, 116(11), 15-21. doi: 10.5120/20213-2825.
  3. Cai, W., & Xu, X. (2012). A Review of Task Scheduling Algorithms in Cloud Computing. International Journal of Grid and Distributed Computing, 5(2), 33-40.
  4. A.S. Pillai, K. Singh, V. Saravanan, A. Anpalagan, I. Woungang, L. Barolli, A genetic algorithm-based method for optimizing the energy consumption and performance of multiprocessor systems. Soft Comput.22(10), 3271–3285 (2018). https://doi.org/10.1007/s00500-017-2789-y
  5. Abusfian Elgelany, Nadar Nada “Energy Efficiency for Data Centre and Cloud Computing: A Literature Review” Sudan University, Khartoum, Sudan, Fatih University, Istanbul, Turkey.
  6. Ankita Choudhary, Shilpa Rana, K.J.Matahai, “A Critical Analysis of Energy Efficient Virtual Machine Placement Techniques and its Optimization in Cloud Computing” Department of CEA, NITTTR, Bhopal, India.
  7. Gagandeep Jagdev et al., “Analyzing Commercial Aspects and Security Concerns Involved in Energy Efficient Cloud Computing” in International Journal of Scientific and Technical Advancements (IJSTA), ISSN-2454-1532, 2016.
  8. Chohan, A., & Raza, S. (2017). Cloud Computing Task Scheduling Algorithms: A Survey. Journal of Cloud Computing: Advances, Systems and Applications, 6(1), 1-16. doi: 10.1186/s13677-017-0087-6.
  9. Gupta, A., & Patel, S. (2015). Resource Management and Scheduling Algorithms for Cloud Computing: A Review. International Journal of Computer Science and Technology, 6(2), 45-51.
  10. Liu, X., & Wang, Z. (2013). Performance Evaluation of Task Scheduling Algorithms in Cloud Computing. Proceedings of the International Conference on Cloud Computing and Big Data (CloudCom-Asia), 155-160.
  11. Liu, X., & Li, Y. (2014). Scheduling Strategies for Cloud Computing: Comparative Analysis. Journal of Cloud Computing: Theory and Applications, 3(4), 1-8. doi: 10.1186/s13677-014-0030-3.
  12. Mishra, R., & Patil, V. (2016). Cloud Computing Task Scheduling Algorithms: An Empirical Analysis. International Journal of Computer Science and Information Technologies, 7(5), 2340-2345.
  13. N. Bacanin, M. Tuba, in IEEE Congress on Evolutionary Computation, CEC 2015. Fireworks algorithm applied to constrained portfolio optimization problem (IEEENew York, 2015), pp. 1242– 1249. https://doi.org/10.1109/CEC.2015.7257031.
  14. deOliveira L.L., Freitas A.A., Tinós R., Multi-objective genetic algorithms in the study of the genetic code’s adaptability. Inf. Sci.425 :, 48–61 (2018). https://doi.org/10.1016/j.ins.2017.10.022 Z. Zheng, N. Saxena, K.K. Mishra, A.K. Sangaiah, Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications. Futur. Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2018.05.027
  15. Gurpreet Kaur & Dr. Gagandeep Jagdev (2017). Implementation of DES and AES Cryptographic Algorithms in Accordance with Cloud Computing, International Journal of Research Studies in Computer Science and Engineering (IJRSCSE), 4(4), pp.1-14, DOI: http://dx.doi.org/10.20431/2349-4859.0404001
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How to Cite

Pramod Kumar Arya, (2025-03-05 23:43:52.810). Analyzing Task Scheduling Algorithms in Cloud Computing for Optimal Performance. JANOLI International Journal of Computer Science and Engineering , Volume rn1ql9uo4BygpjFCAoIa, Issue 1.

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