1. Armbrust M, Fox A, Grith R, et al. Above the clouds: A Berkeley view of cloud computing. UCB/EECS-2009-28. Berkeley, CA,USA: Electrical Engineering and Computer Science, University of California at Berkeley, 2009
2. Hofer C N, Karagiannis G. Cloud computing services: Taxonomy and comparison. Journal of Internet Services and Applications, 2011, 2(2): 81-94
3. Li Q, Zheng X. Research survey of cloud computing. Journal of Computer Science, 2011 38(4): 32-37 (in Chinese)
4. Foster I, Zhao Y, Raicu I, et al. Cloud computing and grid computing 360-degree compare. Proceedings of the Grid Computing Environments Workshop (GCE’08), Nov 12-16, 21008, Austin, TX, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2008:10p
5. Asit K M, Joseph L H, Walfredo C, et al. Towards characterizing cloud backend workloads: Insights from google computing clusters. ACM SIGMETRICS Performance Evaluation Review, 2010 37(4): 34-41
6. Zhang Q, Hellerstein J L, Boutaba R. Characterizing task usage shapes in Google’s computer cluster. Proceedings of the 5th International Workshop on Large Scale Distributed Systems and Middleware (LADIS’11), Sep 2-3, 2011, Seattle, WA, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2011: 6p
7. Sharma B, Rifaat R, Chudnovsky V, et al. Modeling and synthesizing task placement constraints in Google computer cluster. Proceedings of the 2nd ACM Symposium on Cloud Computing (SOCC’11), Oct 27-28, 2011, Cascais, Portugal. New York, NY, USA: ACM, 2011: Article 3
8. Roy N, Dubey A, Gokhale A. Efficient autoscaling in the cloud using predictive models for workload forecasting. Proceedings of the IEEE 4th International Conference on Cloud Computing (Cloud’11), Jul 4-9, 2011, Washington, DC, USA. Washington, DC, USA: IEEE Computer Society, 2011: 500-507
9. Farahnakian F, Liljeberg P, Plosila J. LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers. Proceedings of the 39th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA’13), Sep 4-6, 2013, Santander, Spain. Piscataway, NJ, USA: IEEE, 2013: 357-364
10. Shen Z M, Subbiah S, Gu X H, et al. CloudScale: Elastic resource scaling for multi-tenant cloud system. Proceedings of the 2nd ACM Symposium on Cloud Computing (SOCC’11), Oct 27-28, 2011, Cascais, Portugal. New York, NY, USA: ACM, 2011: Article 5
11. Khan A, Yan X, Tao S, et al. Workload characterization and prediction in the cloud: A multiple time series approach. Proceedings of the 13th IEEE/IFIP Network Operations and Management Symposium (NOMS’12), Apr 16-20, 2012, Maui, HI, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2012: 1287-1294
12. Di S, Kondo D,Cirne W. Host load prediction in a Google compute cloud with a Bayesian model. Proceedings of the 25th International Conference for High Performance Computing, Networking, Storage and Analysis (SC'12), Nov 10-16, 2012, Salt Lake City, UT, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2012: Article 21
13. Liu Z T, Cho S. Characterizing machines and workloads on a Google cluster. Proceedings of the 41st International Conference on Parallel Processing Workshops (ICPPW’12), Sep 10-13.2012, Pittsburgh, PA, USA. Los Alamitos, CA, USA: IEEE Computer Society, 2012: 397-403
14. Davis I J, Hemmati H, Holt R C, et al. Regression-based utilization prediction algorithms: An empirical investigation. Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research (CASCON’13), Nov 18-20, 2013, Toronto, Canada. New York, NY, USA: ACM, 2013:106-120
15. Salvador G, Joaquin D, Jose C, et al. Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 34(3): 417-435
16. Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 1997, 29(2/3): 103-130
17. Berger J O. Statistical decision theory and Bayesian analysis. 2nd ed. Berlin,Germany: Springer-Verlag, 1993
