Statistical modeling of spot instance prices in public cloud environments

Proceedings - 2011 4th IEEE International Conference on Utility and Cloud Computing, UCC 2011

Volumn , Issue , 2011, Pages 219-228

  Javadi, Bahman ^a   Thulasiramy, Ruppa K ^b   Buyya, Rajkumar ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Cloud Computing; Spot Price; Statistical Model

Indexed keywords

COMPETITIVE BIDDING; COMPREHENSIVE ANALYSIS; DATA CENTERS; DATA SERIES; DEGREE OF ACCURACY; EXTENSIVE SIMULATIONS; FAULT-TOLERANT MECHANISM; MIXTURE OF GAUSSIANS; MONETARY COSTS; PRICE CHANGES; SPOT MARKET; SPOT PRICE; STATISTICAL MODEL; STATISTICAL MODELING; STATISTICAL MODELS; STOCHASTIC SCHEDULING; TIME DYNAMIC; WORKING CONDITIONS;

COMPUTER SIMULATION; COSTS; DYNAMICS; ECONOMICS; SCHEDULING ALGORITHMS;

CLOUD COMPUTING;

EID: 84856345948     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/UCC.2011.37     Document Type: Conference Paper

References (14)

1. Cloud exchange website. http://cloudexchange.org/.
2. Amazon Inc. Amazon Discussion Forums. https://forums.aws.amazon.com.
3. Amazon Inc. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2.
4. A. Andrzejak, D. Kondo, and S. Yi. Decision model for cloud computing under SLA constraints. In 18th IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pages 257-266, 2010.
5. 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, 41(1):23-50, 2011.
6. N. Chohan, C. Castillo, M. Spreitzer, M. Steinder, A. Tantawi, and C. Krintz. See spot run: using spot instances for MapReduce workflows. In the 2nd USENIX conference on Hot topics in cloud computing, HotCloud'10, pages 7-7, 2010.
7. C. Fraley and A. E. Raftery. Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458):611-631, 2002.
8. A. Iosup, H. Li, M. Jan, S. Anoep, C. Dumitrescu, L. Wolters, and D. H. J. Epema. The Grid Workloads Archive. Future Generation Computer Systems, 24(7):672-686, 2008.
9. B. Javadi and R. Buyya. Comprehensive statistical analysis and modeling of spot instances in public Cloud environments. Research Report CLOUDS-TR-2011-1, Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, March 2011.
10. B. Javadi, D. Kondo, J.-M. Vincent, and D. P. Anderson. Mining for statistical availability models in large-scale distributed systems: An empirical study of SETI@home. In 17th IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pages 1-10, 2009.
11. D. Kondo, A. Andrzejak, and D. P. Anderson. On correlated availability in internet distributed systems. In 9th IEEE/ACM International Conference on Grid Computing (Grid 2008), pages 276-283, 2008.
12. M. Mattess, C. Vecchiola, and R. Buyya. Managing peak loads by leasing cloud infrastructure services from a spot market. In 12th IEEE International Conference on High Performance Computing and Communications, pages 180-188, 2010.
13. J. Varia. Cloud Computing: Principles and Paradigms, chapter 18: Best Practices in Architecting Cloud Applications in the AWS Cloud, pages 459-490. Wiley Press, 2011.
14. S. Yi, D. Kondo, and A. Andrzejak. Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud. In 3rd IEEE International Conference on Cloud Computing, pages 236 -243, 2010.