In order to realize high scalability, flexibility and cost-effectiveness, cloud computing platforms need to be able to quickly plan and provision resources. To this end, it calls for mechanisms to predict demanded resource effectively. Therefore, resource prediction is a crucial issue for efficient resource utilization in dynamic cloud computing environment. In this paper, the basic concept of generalized fuzzy soft sets is introduced, and a novel angle cosine is proposed based similarity measurement of generalized fuzzy soft sets. Then the similarity measurement result and the prediction accuracy from Adaptive Neuro-Fuzzy Inference System and Seasonal ARIMA model are adopted to obtain the weights of combined prediction model. On this basis,the generalized fuzzy soft sets theory based on the combination of forecasting model GFSS-ANFIS/SARIMA is constrncted. Finally, this model is explorted to predict the demanded resource in cloud computing. The experimental results show that the proposed model can significantly improve the prediction accuracy with high prediction performance. Efficient decision support for resource scheduling and allocation in cloud computing can be provided by the proposed method.
[1] Bianchini R, Rajamony R. Power and energy management for server systems[J]. Computer, 2004, 37(11):68-74.
[2] Guenter B, Jain N, Williams C. Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning[C].Proceedings of the 30th IEEE International Conference on Computer Communications,Shanghai,China,April 10-15,2011.
[3] Baliga J, Ayre R W A, Hinton K,et al.Green cloud computing: Balancing energy in processing, storage, and transport[J]. Proc. IEEE, 2011, 99(1):149 -167.
[4] Garg S K, Yeo C S, Anandasivam A,et al. Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers[J]. Journal of Parallel and Pistributed Computing, 2011, 71(6): 732-749.
[5] Mark C C T,Niyato D,Che-khong T. Evolutionary optimal virtual machine placement and demand forecaster for cloud computing[C].Proceedings of the 25th International Conference on Advanced Information Networking and Applications, Biopolis,Singapore,March 22-25,2011.
[6] Caron E, Desprez F, Muresan A. Forecasting for grid and cloud computing on-demand resources based on pattern matching[C]. Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science,Indianapolis,November 30-December 3,2010.
[7] Islam S, Keung J, Lee K, et al. Empirical prediction models for adaptive resource provisioning in the cloud[J]. Future Generation Computer Systems, 2011,28(1): 155-162.
[8] Duy T V T, Sato Y, Inoguchi, Y. Performance evaluation of a green scheduling algorithm for energy savings in cloud computing[C].Proceeding of International Symposium on Parallel Distributed Processing Workshops and Phd Forum IPDPSW,Atlanta,GA,April 19-23,2010.
[9] Dickinson J P. Some comments on the combination of forecasts[J]. Operational Research Quarterly, 1975,26(1):205-210.
[10] Bates J M, Granger C W J. The combination of forecasts[J]. Operations Research Quarterly, 1969, 20(4): 415-468.
[11] 陈华友,盛昭瀚.一类基于IOWGA算子的组合预测新方法[J].管理工程学报,2005,19(4):36-39.
[12] 孙李红,沈继红.基于相关系数的加权几何平均组合预测模型的性质[J].系统工程理论与实践,2009,(9): 84-91.
[13] 李美娟,陈国宏,林志炳.基于漂移度的组合预测方法研究[J].中国管理科学,2011,19(3):111-117.
[14] Xiao Zhi, Gong Ke, Zou Yan. A combined forecasting approach based on fuzzy soft sets[J]. Journal of computational and Applied Mathematics, 2009,228(1): 326-333.
[15] Molodtsov D. Soft set theory-first results[J]. Computers & Mathematics with Application, 1999,37(4):19-31.
[16] Maji P K, Roy A R, Biswas R. An application of soft sets in a decision making problem[J]. Computers & Mathematics with Application, 2002,44(8):1077-083.
[17] 孙智勇,刘星.模糊软集合理论在税收组合预测中的应用[J].系统工程理论与实践,2011,31(5): 936-943.
[18] Majumdar P, Samanta S K. Generalized fuzzy soft sets[J]. Computers & Mathematics with Application, 2010, 59 (4): 1425-1432.
[19] Xiao Zhi, Yang Xianglei,Niu Qing,et al. A new evaluation method based on D-S generalized fuzzy soft sets and its application in medical diagnosis problem[J]. Applied Mathematical Modelling,2012, 36(10): 4592-4604.
[20] Maji P K, Biswas R, Poy A R.Intuitionistic fuzzy solf sets[J]. Journal of Fuzzy Mathematics,2001, 9(3): 677-692.
[21] Bustince H, Barrenechea E, Pagola M. Image thresholding using restricted equivalence functions and maximizing the measures of similarity[J]. Fuzzy Sets and Systems, 2007, 158(5): 496-516.
[22] Benson T, Akella A, Maltz D A. Network traffic characteristics of data centers in the wild[C].Proceedings of the 10th ACM SIGCOMM conference on Internet measurement,Melbourne,Australia,November 1-3,201 1-3,2010.
[23] Tan Jian, Dube P, Meng Xiaoqiao, et al. Exploiting resource usage patterns for better utilization prediction[C].Proceedings of the 31st International Conference on Distributed Computing Systems Workshops (ICDCSW'11), Minneapolis,Minnesota,USA,June 20-24,2011.
[24] Bezdek J, Pal S. Fuzzy models for pattern recognition[M].New Jersey,USA: IEEE Press, 1992.
[25] Tseng F M, Tzeng G H. A fuzzy seasonal ARIMA model for forecasting[J]. Fuzzy Sets and Systems, 2002, 126(3): 367-376.
[26] Traces in the Internet traffic archive[DB/OL]. http://ita.ee.lbl.gov/html/traces.html.
[27] Mehta A, Menaria M,Dangi S,et al. Envergy conservation in cloud infrastructures[C]. Prceedings of 5th Annual IEEE International Systems Conference, Montreal,Canada,April, 4-11,2011.
[28] Prevost J J, Nagothu K M,Kelley B,et al. Prediction of cloud data center networks loads using stochastic and neural models[C].Proceeding of 6th International Conference on System of Systems Engineering, Albuquerque,USA,dune 27-30,2011.
