Principal Component Regression-Based Adaptive Multiple Extrema Seeking Cat Swarm Resource Optimized Task Scheduling In Cloud Computing


Dr.N.Chenthalir Indra


In this paper, task scheduling process is a challenging task in cloud computing to determine the best optimal virtual machine for each task. Many types of scheduling algorithms have been introduced for small or medium-scale cloud computing. However, dynamic scheduling is a major challenging problem for large-scale cloud computing environments. To address the issue, this paper proposes a novel technique called Principal Component Regression-based Adaptive Multiple Extrema Seeking Cat Swarm Resource Optimization (PCR-AMESCSRO) technique for efficient task scheduling with lesser makespan and higher efficiency. The PCR-AMESCSRO technique is designed with the contribution of Principal Component Regression (PCR) and Adaptive Multiple Extrema Seeking Cat Swarm Optimization Algorithm (AMESCSOA). First, the PCR is applied to analyze the user requested task and assign the priority level with lesser makespan. Second, the AMESCSOA is used to identify the optimal virtual machine by the cloud manager. Lastly, the experimental valuation is performed on factors such as task scheduling efficiency, false-positive rate, makespan, and memory consumption with respect to a number of user tasks. The observed results show the superior performance of our proposed PCR-AMESCSRO technique when compared to state-of-the-art methods.


How to Cite
KM, A., & Dr.N.Chenthalir Indra. (2022). Principal Component Regression-Based Adaptive Multiple Extrema Seeking Cat Swarm Resource Optimized Task Scheduling In Cloud Computing. International Journal of Next-Generation Computing, 13(2).


  1. Ajitha, K., Chenthalir, N., and Hindu, S. 2020. Bivariate correlative oppositional based
  2. artificial fish swarm resource optimized task scheduling in cloud. International Journal of
  3. Next-Generation Computing 11, 2, 163–177.
  4. Amit Kaushal, S. S. 2019. Performance optimization by task scheduling in cloud computing.
  5. International Journal of Engineering and Advanced Technology 9, 20–24.
  6. Ben Alla, H., Ben Alla, S., and Ezzati, A. 2020. A dynamic task scheduling algorithm for
  7. cloud computing environment. Recent Advances in Computer Science and Communications
  8. (Formerly: Recent Patents on Computer Science) 13, 2, 296–307.
  9. Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., and Murphy, J. 2020. A woabased
  10. optimization approach for task scheduling in cloud computing systems. IEEE Systems
  11. Journal 14, 03, 3117–3128.
  12. Domanal, S., Guddeti, R., and Buyya, R. 2020. A hybrid bio-inspired algorithm for scheduling
  13. and resource management in cloud environment. IEEE Transactions on Services Computing
  14. , 1, 3–15.
  15. Ebadifard, F. and Babamir, S. M. 2018. A pso-based task scheduling algorithm improved
  16. using a load-balancing technique for the cloud computing environment. Concurrency and
  17. Computation: Practice and Experience 30, 12, 1–16.
  18. Gupta and N. Koul, "SWAN: A Swarm Intelligence Based Framework for Network Management of IP Networks," International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), 2007, pp. 114-118, doi: 10.1109/ICCIMA.2007.63 DOI:
  19. Gao, Z., Wang, Y., Gao, Y., and Ren, X. 2020. Multiobjective noncooperative game model
  20. for cost-based task scheduling in cloud computing. Concurrency and Computation: Practice
  21. and Experience 32, 7, e5570.
  22. Hasan, M. Z. and Al-Rizzo, H. 2020. Task scheduling in internet of things cloud environment
  23. using a robust particle swarm optimization. Concurrency and Computation: Practice and
  24. Experience 32, 2, 1–17.
  25. Hoseinnejhad, M. and Navimipour, N. J. 2017. Deadline constrained task scheduling in the
  26. cloud computing using a discrete firey algorithm. International Journal of Next-Generation
  27. Computing 8, 3, 198–209.
  28. Jacob, T. P. and Pradeep, K. 2019. A multi-objective optimal task scheduling in cloud
  29. environment using cuckoo particle swarm optimization. Wireless Personal Communications
  30. , 1, 315–331.
  31. Kashikolaei, S. M. G., Hosseinabadi, A. A. R., Saemi, B., Shareh, M. B., Sangaiah,
  32. A. K., and Bian, G.-B. 2020. An enhancement of task scheduling in cloud computing
  33. based on imperialist competitive algorithm and firefly algorithm. The Journal of Supercomputing
  34. , 8, 6302–6329.
  35. Khorsand, R. and Ramezanpour, M. 2020. An energy-efficient task-scheduling algorithm
  36. based on a multi-criteria decision-making method in cloud computing. International Journal
  37. of Communication Systems 33, 9, 1–17.
  38. Lin, W., Peng, G., Bian, X., Xu, S., Chang, V., and Li, Y. 2019. Scheduling algorithms
  39. for heterogeneous cloud environment: main resource load balancing algorithm and time
  40. balancing algorithm. Journal of Grid Computing 17, 4, 699–726.
  41. Madni, S. H. H., Abd Latiff, M. S., Ali, J., et al. 2019. Multi-objective-oriented cuckoo
  42. search optimization-based resource scheduling algorithm for clouds. Arabian Journal for
  43. Science and Engineering 44, 4, 3585–3602.
  44. Mansouri, N., Zade, B. M. H., and Javidi, M. M. 2019. Hybrid task scheduling strategy
  45. for cloud computing by modified particle swarm optimization and fuzzy theory. Computers
  46. & Industrial Engineering 130, 597–633.
  47. Pang, S., Li, W., He, H., Shan, Z., and Wang, X. 2019. An eda-ga hybrid algorithm for
  48. multi-objective task scheduling in cloud computing. IEEE Access 7, 146379–146389.
  49. Pradeep, K. and Jacob, T. P. 2018. A hybrid approach for task scheduling using the cuckoo
  50. and harmony search in cloud computing environment. Wireless Personal Communications
  51. , 4, 2287–2311.
  52. Sanaj, M. and Prathap, P. J. 2020. Nature inspired chaotic squirrel search algorithm (cssa)
  53. for multi objective task scheduling in an iaas cloud computing atmosphere. Engineering
  54. Science and Technology, an International Journal 23, 4, 891–902.
  55. Singh, H., Tyagi, S., and Kumar, P. 2020. Crow–penguin optimizer for multiobjective task
  56. scheduling strategy in cloud computing. International Journal of Communication Systems
  57. , 14, e4467.
  58. Xiong, Y., Huang, S., Wu, M., She, J., and Jiang, K. 2019. A johnson’s-rule-based genetic
  59. algorithm for two-stage-task scheduling problem in data-centers of cloud computing. IEEE
  60. Transactions on Cloud Computing 7, 03, 597–610.
  61. Xu, J., Hao, Z., Zhang, R., and Sun, X. 2019. A method based on the combination of laxity
  62. and ant colony system for cloud-fog task scheduling. IEEE Access 7, 116218–116226.
  63. Zhang, H., Wu, Y., and Sun, Z. 2021. Eheft-r: multi-objective task scheduling scheme in
  64. cloud computing. Complex & Intelligent Systems, 1–8.
  65. Zhang, P. and Zhou, M. 2017. Dynamic cloud task scheduling based on a two-stage strategy.
  66. IEEE Transactions on Automation Science and Engineering 15, 2, 772–783.