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Autonomic Cloud Resource Allocation Method based on LS-SVM and Virtual Allocation

Volume 14, Number 9, September 2018, pp. 1958-1967
DOI: 10.23940/ijpe.18.09.p3.19581967

Chenyang Zhaoa and Junling Wangb

aCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, China
bCollege of Science, Henan University of Technology, Zhengzhou, 450001, China

(Submitted on May 21, 2018; Revised on July 16, 2018; Accepted on August 10, 2018)


Current cloud resource allocation cannot be performed autonomously. When a cloud server overloads, the task queue continues to grow, which leads to delay or failure of task execution. In order to solve this problem, an autonomic cloud resource allocation method is proposed in this paper. For each type of task, Least Squares Support Vector Machine (LS-SVM) is used to predict the number of upcoming tasks in the next period by analyzing a time series of historical task numbers. Meanwhile, the queue lengths of various types of tasks are also periodically monitored during each period. Then, according to the predicted task numbers and the real-time queue lengths, Virtual Allocation (VA) is used to autonomously adjust resource allocation for various types of tasks during the task execution. The experiment shows that LS-SVM prediction is more accurate and VA is more effective, which can improve loads of cloud servers and reduce completion time of tasks.


References: 18

              1. L. Vaquero, M. L. Rodero, J. Caceres, and M. Lindner, “A Break in the Clouds: Towards a Cloud Definition,” ACM SIGCOMM Computer Communication Review, Vol. 39, No. 1. pp. 50-55, January 2008
              2. A. Weiss, “Computing in the Cloud,” netWorker, Vol. 11, No. 4, pp. 16-26, October 2007
              3. Y. Ding, X. Qin, L. Liu, and T. Wang, “Energy Efficient Scheduling of Virtual Machines in Cloud with Deadline Constraint,” Future Generation Computer Systems, Vol. 50, No. C, pp. 62-74, September 2015
              4. N. Zhang, X. Yang, M. Zhang, Y. Sun, and K. Long, “A Genetic Algorithm-based Task Scheduling for Cloud Resource Crowd-Funding Model,” International Journal of Communication Systems, Vol. 5, No. 1, e3394, September 2017
              5. F. Ebadifard and S. M. Babamir, “A PSO-based Task Scheduling Algorithm Improved using a Load-Balancing Technique for the Cloud Computing Environment,” Concurrency & Computation Practice & Experience, Vol. 30, No. 1, e4368, December 2017
              6. S. Singh and V. Singh, “A Genetic based Improved Load Balanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing,” in Proceedings of the 8th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 677-681, Tehri, India, December 2016
              7. C. Y. Zhao, S. B. Li, Y. Yang, and J. L. Wang, “An Autonomic Performance-Aware Workflow Job Management for Service-Oriented Computing,” in Proceedings of the 9th International Conference on Grid and Cooperative Computing (GCC), pp. 270-275, NanJing, China, November 2011
              8. A. V. Lakra and D. K. Yadav, “Multi-Objective Tasks Scheduling Algorithm for Cloud Computing Throughput Optimization,” Procedia Computer Science, Vol. 48, pp. 107-113, December 2015
              9. M. A. Vasile, F. Pop, R. I. Tutueanu, and V. Cristea, “Resource-Aware Hybrid Scheduling Algorithm in Heterogeneous Distributed Computing,” Future Generation Computer Systems, Vol. 51, No. C, pp. 61-71, October 2015
              10. X. You, X. Xu, J. Wan, and D. Yu, “RAS-M: Resource Allocation Strategy based on Market Mechanism in Cloud Computing,” Journal of Computers, Vol. 6, No. 7, pp. 256-263, August 2011
              11. S. F. Ding and B. J. Qi, “Research of Granular Support Vector Machine,” Artificial Intelligence Review, Vol. 38, No. 1, pp. 1-7, May 2012
              12. Y. M. Lu and V. Roychowdhury, “Parallel Randomized Sampling for Support Vector Machine (SVM) and Support Vector Regression (SVR),” Knowledge and Information Systems, Vol. 14, No. 2, pp. 233-247, January 2008
              13. J. Liu and T. Y. Tan, “LS-SVM based Substation Circuit Breakers Maintenance Scheduling Optimization,” International Journal of Electrical Power & Energy Systems, Vol, 64. pp. 1251-1258, January 2015
              14. Q. Gan and J. H. Zheng, “A New Algorithm to Improve Efficiency of Resource Scheduling in Clouding Computing based on Extended Support Vector Machine,” International Journal of Grid and Distributed Computing, Vol. 9, No. 3, pp. 125-134, March 2016
              15. J. J. Hu, X. L. Chen, and C. Y. Zhang, “Proactive Service Selection based on Acquaintance Model and LS-SVM,” Neurocomputing, Vol. 211, pp. 60-65, October 2016
              16. M. C. Huebscher and J. A. Mccann, “A Survey of Autonomic Computing-Degrees, Models, and Applications,” ACM Computing Surveys, Vol. 40, No. 3, pp. 1-28, August 2008
              17. T. T. Chen and S. J. Lee, “A Weighted LS-SVM based Learning System for Time Series Forecasting,” Information Sciences, Vol. 299, No. C, pp. 99-116, April 2015
              18. Y. Bodyanskiy and O. Vynokurova, “Hybrid Adaptive Wavelet-Neuro-Fuzzy System for Chaotic Time Series Identification,” Information Sciences, Vol. 220, No. 1, pp. 170-179, January 2013


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