Abstract
In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation for deploying a set of services over a federated cloud computing infrastructure by considering both, the availability and the demand of the cloud computing resources. This distributed control based approach is developed by using an interaction balance (decomposition-coordination) methodology for interactive bidding of computing resources in cloud computing environment. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for both, the service providers and the cloud brokers. The cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers to sublet the cloud resources that the cloud brokers rent or lease from a number of cloud providers. The developed approach is novel in applying interaction balance methodology, and giving priority to the profit maximization for both the cloud broker and service providers, while assigning the cloud computing resources.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdelwahed, S., Bai, J., Su, R., Kandasamy, N.: On the application of predictive control techniques for adaptive performance management of computing systems. IEEE Trans. Netw. Serv. Manag. 6(4), 212–225 (2009)
Arianyan, E., Maleki, D., Yari, A., Arianyan, I.: Efficient resource allocation in cloud data centers through genetic algorithm. In: 2012 Sixth International Symposium on Telecommunications (IST), pp. 566–570, November 2012
Arlitt, M., Jin, T.: Workload characterization of the 1998 world cup web site. Technical report HPL-99-35R1, Hewlett-Packard Labs, September 1999
DeLurgio, S.A.: Forecasting Principles and Applications. McGraw-Hill, New York (1998)
Dinesh, K., Poornima, G., Kiruthika, K.: Efficient resources allocation for different jobs in cloud. Int. J. Comput. Appl. 56, 30–35 (2012)
Amazon EC2. Amazon elastic compute cloud, March 2012. http://aws.amazon.com/ec2/
Google. Apps, March 2012. http://www.google.com/apps/intl/en/business/index.html
Gouda, K.C., Radhika, T.V., Akshatha, M.: Priority based resource allocation model for cloud computing. Int. J. Sci. Eng. Technol. Res. (IJSETR) 2(1), 215–219 (2013)
Healey, M.: State of cloud 2011: Time for process maturation, January 2011. http://reports.informationweek.com/abstract/5/5116/Cloud-Computing/research-2011-state-of-cloud.html, March 2012
IBM. Smart cloud, March 2012. http://www.ibm.com/cloud-computing/us/en/
Jain, P., Rane, D., Patidar, S.: A novel cloud bursting brokerage and aggregation (cbba) algorithm for multi cloud environment. In: 2012 Second International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 383–387. IEEE (2012)
Jebalia, M., Letaïfa, A.B., Hamdi, M., Tabbane, S.: A comparative study on game theoretic approaches for resource allocation in cloud computing architectures. In: IEEE 22nd International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2013, pp. 336–341. IEEE (2013)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82(D), 35–45 (1960)
Kandasamy, N., Abdelwahed, S., Khandekar, M.: A hierarchical optimization framework for autonomic performance management of distributed computing systems. In: Proceedings 26th IEEE International Conference on Distributed Computing Systems (ICDCS) (2006)
Mehrotra, R., Abdelwahed, S.: Towards autonomic performance management of large scale data centers using interaction balance principle. Cluster Comput. 17(3), 979–999 (2014). doi:10.1007/s10586-013-0333-0
Mehrotra, R., Abdelwahed, S., Erradi, A.: A distributed control approach for autonomic performance management in cloud computing environment. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, UCC ’13, Washington, DC, USA, pp. 269–272. IEEE Computer Society (2013)
Mehrotra, R., Dubey, A., Abdelwahed, S., Tantawi, A.: A power-aware modeling and autonomic management framework for distributed computing systems. In: Ranka, S., Ahmad, I. (eds.) Handbook of Energy-Aware and Green Computing, p. 38. CRC Press, Boca Raton (2011)
Nair, S.K., Porwal, S., Dimitrakos, T., Ferrer, A.J., Tordsson, J., Sharif, T., Sheridan, C., Rajarajan, M., Khan, A.U.: Towards secure cloud bursting, brokerage and aggregation. In: 2010 IEEE 8th European Conference on Web Services (ECOWS), pp. 189–196. IEEE (2010)
Pawar, C.S., Wagh, R.B.: Priority based dynamic resource allocation in cloud computing. In: 2012 International Symposium on Cloud and Services Computing (ISCOS), pp. 1–6. IEEE (2012)
Search Cloud Provider. Cloud broker, April 2014. http://searchcloudprovider.techtarget.com/definition/cloud-broker
Rogers, O., Cliff, D.: A financial brokerage model for cloud computing. J. Cloud Comput. 1(1), 1–12 (2012)
Roy, N., Dubey, A., Gokhale, A., Dowdy, L.: A capacity planning process for performance assurance of component-based distributed systems (abstracts only). SIGMETRICS Perform. Eval. Rev. 39(3), 16–17 (2011)
Sadati, N.: A novel approach to coordination of large-scale systems; part ii interaction balance principle. In: IEEE International Conference on Industrial Technology, pp. 648–654, December 2005
Sundareswaran, S., Squicciarini, A., Lin, D.: A brokerage-based approach for cloud service selection. In: 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 558–565. IEEE (2012)
Singh, M.G., Titli, A.: Systems Decomposition, Optimisation, and Control. Pergamon Press, Oxford (1978)
Windows. Azure, March 2012. http://www.windowsazure.com
Wu, L., Garg, S.K., Buyya, R.: Sla-based resource allocation for software as a service provider (saas) in cloud computing environments. In: 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 195–204. IEEE (2011)
Zaman, S., Grosu, D.: Combinatorial auction-based allocation of virtual machine instances in clouds. J. Parallel Distrib. Comput. 73(4), 495–508 (2013)
Acknowledgments
This work was supported in part by the National Science Foundation (NSF) under grant numbers NSF IIP-\(1127978\) and NSF IIP-\(1034897\) at the NSF Center for Cloud and Autonomic Computing, Mississippi State University, and by C-FAR, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA at the University of Virginia, Charlottesville, VA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mehrotra, R., Srivastava, S., Banicescu, I., Abdelwahed, S. (2014). An Interaction Balance Based Approach for Autonomic Performance Management in a Cloud Computing Environment. In: Pop, F., Potop-Butucaru, M. (eds) Adaptive Resource Management and Scheduling for Cloud Computing. ARMS-CC 2014. Lecture Notes in Computer Science(), vol 8907. Springer, Cham. https://doi.org/10.1007/978-3-319-13464-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-13464-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13463-5
Online ISBN: 978-3-319-13464-2
eBook Packages: Computer ScienceComputer Science (R0)