A dynamic resource scheduling method based on fuzzy control theory in cloud environment

Journal of Control Science and Engineering

Volumn 2015, Issue , 2015, Pages

  Chen, Zhijia ^a   Zhu, Yuanchang ^a   Di, Yanqiang ^a   Feng, Shaochong ^a  

^a MECHANICAL ENGINEERING COLLEGE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLOUD COMPUTING; COMPUTATION THEORY; DISTRIBUTED COMPUTER SYSTEMS; FUZZY CONTROL; QUALITY OF SERVICE; SCHEDULING;

CLOUD COMPUTING SERVICES; CLOUD ENVIRONMENTS; DYNAMIC RESOURCE SCHEDULING; PREDICTION MODEL; RESOURCE AVAILABILITY; RESOURCE REQUIREMENTS; RESOURCES SCHEDULING; USER REQUIREMENTS;

CONTROL THEORY;

EID: 84941243926     PISSN: 16875249     EISSN: 16875257     Source Type: Journal    
DOI: 10.1155/2015/383209     Document Type: Article

References (24)

1. S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, andA. Ghalsasi, "Cloud computing-the business perspective, "Decision Support Systems, vol. 51, no. 1, pp. 176-189, 2011.
2. X.-L. Shi and K. Xu, "Utility maximization model of virtual machine scheduling in cloud environment, " Chinese Journal of Computers, vol. 36, no. 2, pp. 252-262, 2013.
3. G. Zhao, H. Yu, T. Ji et al. , "Adaptive resource provisioning for cloud computing, " Telecommunications Science, no. 1, pp. 31-37, 2012.
4. Z. Jing, Fuzzy Control and SystemsTheory, Mechanical Industry Press, Beijing, China, 2005.
5. A. Ghodsi, M. Zaharia, S. Shenker, and I. Stoica, "Choosy: max-min fair sharing for datacenter jobs with constraints, " in Proceedings of the 8th ACM European Conference on Computer Systems, pp. 365-378, April 2013.
6. Y. Fang, F. Wang, and J. Ge, "A task scheduling algorithmbased on load balancing in cloud computing, " in Web Information Systems and Mining: International Conference, WISM 2010, Sanya, China, October 23-24, 2010. Proceedings, vol. 6318 of LectureNotes in Computer Science, pp. 271-277, Springer, Berlin, Germany, 2010.
7. W. Yan, W. Jinkuan, and W. Cuirong, "Research on resource scheduling of cloud based on improved particle swarm optimization algorithm, " in Proceedings of the International Conference on Brain Inspired Cognitive Systems, pp. 118-125, 2013.
8. L. Huang, H.-S. Chen, andT.-T. Hu, "Survey on resource allocation policy and job scheduling algorithms of cloud computing, " Journal of Software, vol. 8, no. 2, pp. 480-487, 2013.
9. Z. Wu, X. Liu, Z. Ni, D. Yuan, and Y. Yang, "A market-oriented hierarchical scheduling strategy in cloud workflow systems, " Journal of Supercomputing, vol. 63, no. 1, pp. 256-293, 2013.
10. B. Xu, C. Zhao, E. Hu, and B. Hu, "Job scheduling algorithm based on Berger model in cloud environment, " Advances in Engineering Software, vol. 42, no. 7, pp. 419-425, 2011.
11. Y. B. Ma, S. H. Jang, and J. S. Lee, "Ontology-based resource management for cloud computing, " in Intelligent Information and Database Systems: Third International Conference, ACIIDS 2011, Daegu, Korea, April 20-22, 2011, Proceedings, Part II, vol. 6592 of Lecture Notes in Computer Science, pp. 343-352, Springer, Berlin, Germany, 2011.
12. D.-W. Sun, G.-R. Chang, F.-Y. Li, C. Wang, and X.-W. Wang, "Optimizingmulti-dimensionalQoS cloud resource scheduling by immune clonal with preference, " Chinese Journal of Electronics, vol. 39, no. 8, pp. 1824-1831, 2011.
13. E.-K. Byun, Y.-S. Kee, J.-S. Kim, and S. Maeng, "Cost optimized provisioning of elastic resources for application workflows, " Future Generation Computer Systems, vol. 27, no. 8, pp. 1011-1026, 2011.
14. P. Rattanatamrong, Real-time scheduling of ensemble systems with limited resources [Ph. D. thesis], University of Florida, Gainesville, Fla, USA, 2011.
15. M. Luo, K. Zhang, L. Yao, and X. Zhou, "Research on resources scheduling technology based on fuzzy clustering analysis, " in Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 12), pp. 152-155, May 2012.
16. P. Rattanatamrong and J. A. B. Fortes, "Fuzzy scheduling of real-time ensemble systems, " in Proceedings of the International Conference on High Performance Computing and Simulation (HPCS 14), pp. 146-153, IEEE, Bologna, Italy, July 2014.
17. M. Masmoudi and A. Hä?t, "Project scheduling under uncertainty using fuzzy modelling and solving techniques, " Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 135-149, 2013.
18. S. Frey, C. Luthje, V. Huwwa et al. , "Fuzzy controled QoS for scalable cloud computing services, " in Proceedings of the the 4th International Conference on Cloud Computing, GRIDs, and Virtualization, pp. 150-155, 2013.
19. F. Ramezani, J. Lu, and F. Hussain, "An online fuzzy decision support system for resource management in cloud environments, " in Proceedings of the IFSA World Congress and NAFIPS Annual Meeting, pp. 754-759, Edmonton, Canada, June 2013.
20. P. Qi and L.-S. Li, "Task scheduling algorithm based on fuzzy quotient space theory in cloud environment, " Journal of Chinese Computer Systems, vol. 34, no. 8, pp. 1793-1797, 2013.
21. H. Han, Q. Deyui, W. Zheng, and F. Bin, "A Qos guided task scheduling model in cloud computing environment, " in Proceedings of the 4th International Conference on Emerging Intelligent Data and Web Technologies, pp. 72-76, September 2013.
22. Y. J. Huang, T. C. Kuo, and H. K. Lee, "Fuzzy-PD controller design with stability equations for electro-hydraulic servo systems, " in Proceedings of the International Conference on Control, Automation and Systems (ICCAS 07), pp. 2407-2410, October 2007.
23. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, " Software: Practice and Experience, vol. 41, no. 1, pp. 23-50, 2011.
24. Data Flow Statistics and Analysis, 2014,. http://tongji.cnzz.com/.