Date of Publication :13th March 2017
Abstract: Resource providers on Cloud offer heterogeneous resources such as compute units and storage in Virtual Machine instances (VM). Cloud providers expect users to request for resources. In this process, overestimation of resources by cloud users lead to unused resources. The cumulative unutilized resources for each job of the user, amount to unnecessary expenditure for users and wasted resources for providers. Large scale data centers that provide reliable high performance computational and storage services for Cloud providers, face problems of increased energy consumption and CO2 emission as a consequence of huge resource wastage. Therefore, for environmental and financial reasons it is imperative to reduce unnecessary resource reservation. This can be done by using resource prediction technique that ensures resource allocation only as much as is necessary for the customer. Currently, there are no suitable prediction techniques for Cloud resource usage because of absence of pattern, trend and seasonality in users’ resource usage [2].The proposed prediction approach applies Enhanced Instance Based Learning and is tested using Google Cluster Trace Data [1] . The contribution of this work to existing body of work on cloud resource management by effective resource prediction approaches are discussed.
Reference :
-
- Reiss, Charles, John Wilkes, and Joseph L. Hellerstein. "Google cluster-usage traces: format+ schema." Google Inc., White Paper (2011): 1-14.
- C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M.Kozuch, “ Heterogeneity and dynamicity of clouds at scale: Google trace analysis,” in Proceedings of the Third ACM Symposium on Cloud Computing. ACM, 2012, p. 7.
- A. E. C. Cloud, “Web page at http://aws. amazon. com/ec2,”
- R. Bryant, A. Tumanov, O. Irzak, Scannell, K. Joshi, M. Hiltunen, A. Lagar-Cavilla, and E. De Lara, “Kaleidoscope: cloud microelasticity via vm state coloring,” in Proceedings of the sixth conference on Computer systems. ACM, 2011, pp. 273–286.
- H. Nguyen, Z. Shen, X. Gu, S. Subbiah, and J. Wilkes, “Agile: Elastic distributed resource scaling for infrastructure as a service,” in Proc. of the USENIX International Conference on Automated Computing (ICACAZ´13). San Jose, CA, 2013.
- A. Cassandra, “Apache cassandra,” 2013.
- T. Clark, “Rightscale,” 2010
- J. Hamilton, “Cooperative expendable micro-slice servers (cems): low cost, low power servers for internetscale services,” in Conference on Innovative Data Systems Research CIDRA˘´09)(January 2009), 2009
- A. Ali-Eldin, M. Kihl, J. Tordsson, and E. Elmroth, “Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control,” in Proceedings of the 3rd workshop on Scientific Cloud Computing Date.ACM, 2012, pp. 31–40.
- I. Foster, Y. Zhao, I. Raicu, and S. Lu, “Cloud computing and grid computing 360-degree compared,” in Grid Computing Envi- ronments Workshop, 2008. GCE’08.IEEE, 2008, pp. 1–10.
- W. Wang, H. Chen, and X. Chen, “An availability aware virtual machine placement approach for dynamic scaling of cloud applications,” in Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing 9th International Conference on. IEEE, 2012, pp. 509–516.
- T. Redkar and T. Guidici, Windows Azure Platform. Springer, 2009.
- A. Singhai, S. Lim, and S. R. Radia, “The scalr framework for internet services,” in Proceedings of the 28th Fault-Tolerant Com- puting Symposium (FTCS-28), page (to appear), 1998.
- Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, “Cloudscale: elastic resource scaling for multi-tenant cloud systems,” in Proceedings of the 2nd ACM Symposium on Cloud Computing.ACM, 2011, p. 5.
- C. You and K. Chandra, “Time series models for internet data traffic,” in Local Computer Networks, 1999. LCN’99. Conference on. IEEE, 1999, pp. 164–171.
- P. J. Brockwell, Introduction to time series and forecasting. Taylor & Francis, 2002, vol. 1.
- C. Chatfield, The analysis of time series: an introduction.CRC press, 2013.
- P. A. Dinda and D. R. O’Hallaron, “Host load prediction using linear models,” Cluster Computing, vol. 3, no. 4, pp. 265–280, 2000.
- W. Fang, Z. Lu, J. Wu, and Z. Cao, “Rpps: A novel resource prediction and provisioning scheme in cloud data center,” in Services Computing (SCC), 2012 IEEE Ninth International Conference on. IEEE, 2012, pp. 609–616.
- J. Huang, C. Li, and J. Yu, “Resource prediction based on double exponential smoothing in cloud computing,” in Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conf. on. IEEE, 2012, pp. 2056– 2060.
- A. Ali-Eldin, J. Tordsson, E. Elmroth, and M. Kihl, “Workload classification for efficient auto-scaling of cloud resources,” Technical Report, 2005.[Online]. Available: http://www8. cs. umue/research/uminf/reports/2013/013/part1.pdf, Tech. Rep., 2013.
- J. Geweke and C. Whiteman, “Bayesian forecasting,” Handbook of economic forecasting, vol. 1, pp. 3–80, 2006.
- S. Kundu, R. Rangaswami, A. Gulati, M. Zhao, and K. Dutta, “Modeling virtualized applications using machine learning tech- niques,” in ACM SIGPLAN Notices, vol. 47, no.7. ACM, 2012, pp. 3–14.
- L. Aranildo Rodrigues, P. S. de Mattos Neto, and T. Ferreira, “A prime step in the time series forecasting with hybrid methods: The fitness function choice,” in Neural Networks, 2009. IJCNN 2009. International Joint Conference on. IEEE, 2009, pp. 2703– 2710.