Long-term SLOs for reclaimed cloud computing resources

Proceedings of the 5th ACM Symposium on Cloud Computing, SOCC 2014

Volumn , Issue , 2014, Pages

  Carvalho, Marcus ^a   Cirne, Walfredo ^b   Brasileiro, Francisco ^a   Wilkes, John ^b  

^a FEDERAL UNIVERSITY OF CAMPINA GRANDE   (Brazil)

^b GOOGLE INC   (United States)

Author keywords

Capacity planning; Cloud computing; Quality of Service

Indexed keywords

QUALITY OF SERVICE;

CAPACITY PLANNING; CONFIDENCE LEVELS; CURRENT RESPONSE; ON DEMANDS; SERVICE LEVEL OBJECTIVE; SERVICE PROVIDER; WORKLOAD VARIATION;

CLOUD COMPUTING;

EID: 85118316751     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2670979.2670999     Document Type: Conference Paper

References (27)

1. Amazon EC2 FAQs - how many instances can i run in Amazon EC2? "http://aws.amazon.com/ec2/faqs/#How-many-instances-can-I-run-in-Amazon-EC2", Apr. 2014.
2. Amazon EC2 service level agreement. "http://aws.amazon.com/ec2/sla/", Apr. 2014.
3. Amazon EC2 instance purchasing options. "https://aws.amazon.com/ec2/purchasing-options", Apr. 2014.
4. G. E. P. Box and G. Jenkins. Time Series Analysis, Forecasting and Control. Holden-Day, Incorporated, 1990. ISBN 0816211043.
5. K. P. Burnham and D. R. Anderson. Model selection and multimodel inference: a practical information-theoretic approach. Springer, 2 edition, 2002. ISBN 0387953647.
6. J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle. Managing energy and server resources in hosting centers. In ACM Symposium on Operating Systems Principles (SOSP), pages 103-116, 2001.
7. C. Chatfield. 5. forecasting. In The analysis of time series: an introduction. CRC Press, Boca Raton, FL, USA, 6th edition, 2004.
8. Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam. Managing server energy and operational costs in hosting centers. In ACM International Conference on Measurement and Modeling of Computer Systems (SIGMET-RICS), pages 303-314, 2005.
9. R. Costa, F. Brasileiro, G. Lemos, and D. Sousa. Analyzing the impact of elasticity on the profit of cloud computing providers. Future Generation Computer Systems (FGCS), 29(7):1777-1785, Sept. 2013.
10. S. Di, D. Kondo, and W. Cirne. Characterization and comparison of cloud versus Grid workloads. In International Conference on Cluster Computing (IEEE CLUSTER), pages 230-238, Beijing, China, Sept. 2012.
11. S. Di, D. Kondo, and W. Cirne. Host load prediction in a Google compute cloud with a Bayesian model. In International Conference on High Performance Computing, Networking, Storage and Analysis (SC), pages 21:1-21:11, Salt Lake City, UT, USA, Nov. 2012. ISBN 978-1-4673-0804-5.
12. S. Di, D. Kondo, and C. Franck. Characterizing cloud applications on a Google data center. In 42nd International Conference on Parallel Processing (ICPP), Lyon, France, Oct. 2013.
13. Y. Fu, J. S. Chase, B. N. Chun, S. Schwab, and A. Vahdat. SHARP: an architecture for secure resource peering. In ACM Symposium on Operating Systems Principles (SOSP), pages 133-148, 2003.
14. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Capacity management and demand prediction for next generation data centers. In IEEE International Conference on Web Services (ICWS), pages 43-50, July 2007.
15. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Resource pool management: Reactive versus proactive or lets be friends. Computer Networks, 53(17):2905-2922, 2009. ISSN 1389-1286.
16. Z. Gong, X. Gu, and J. Wilkes. PRESS: PRedictive Elastic ReSource Scaling for cloud systems. In International Conference on Network and Service Management (CNSM), pages 9-16, 2010.
17. R. J. Hyndman and G. Athanasopoulos. 7. exponential smoothing. In Forecast: principles and practice. OTexts, 2013.
18. R. J. Hyndman and Y. Khandakar. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 27(3):1-22, July 2008. ISSN 1548-7660.
19. R. J. Hyndman, A. B. Koehler, J. K. Ord, and R. D. Snyder. Prediction intervals for exponential smoothing using two new classes of state space models. Journal of Forecasting, 24(1): 17-37, 2005.
20. P. Marshall, K. Keahey, and T. Freeman. Improving utilization of infrastructure clouds. In IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pages 205-214, 2011.
21. X. Meng, C. Isci, J. Kephart, L. Zhang, E. Bouillet, and D. Pendarakis. Efficient resource provisioning in compute clouds via VM multiplexing. In IEEE/ACM International Conference on Autonomic Computing and Communications (ICAC), pages 11-20, Washington, DC, USA, 2010. ACM.
22. A. K. Mishra, J. L. Hellerstein, W. Cirne, and C. R. Das. Towards characterizing cloud backend workloads: insights from Google compute clusters. SIGMETRICS Performance Evaluation Review, 37(4):34-41, Mar. 2010.
23. F. J. A. Morais, F. V. Brasileiro, R. V. Lopes, R. A. Santos, W. Satterfield, and L. Rosa. Autoflex: Service agnostic auto-scaling framework for IaaS deployment models. In IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pages 42-49, 2013.
24. C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch. Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In ACM Symposium on Cloud Computing (SoCC), pages 7:1-7:13, San Jose, CA, USA, 2012.
25. Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. CloudScale: elastic resource scaling for multi-tenant cloud systems. In J. S. Chase and A. E. Abbadi, editors, ACM Symposium on Cloud Computing (SoCC), page 5, 2011.
26. C. A. Waldspurger. Memory resource management in VMware ESX Server. In OS Design and Implementation (OSDI02), pages 181-194, Dec. 2002.
27. Q. Zhang, J. Hellerstein, and R. Boutaba. Characterizing task usage shapes in Google compute clusters. In Workshop on Large Scale Distributed Systems and Middleware (LADIS), Seattle, WA, USA, Sept. 2011.