Abstract
Virtualization in Cloud data center, handle workloads and maintain SLA providing a better QoS to the Cloud consumer will lead to the harnessing of the present Cloud Computing infrastructure. Our model is on a statistical property and based on reliability and reputation combined for a “trust” based that we design our algorithms to handle QoS and these algorithms prove better than the existing model. However, the growing demand of the resources (physical) in a data center has drastically increased the energy consumption of computations (cyber) being processed in data centers, which has become a decisive issue. To address the trade-off between performance and power consumption we propose a near-optimal scheduling policy based on the CQR (Composite Quantile Regression) and the Minimum energy heuristics (MPP) to find a trust based Cloud character probability modeling that exploits heterogeneity across multiple data centers for a Cloud provider.
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
Preview
Unable to display preview. Download preview PDF.
References
Pettey, C.: Gartner estimates ICT industry accounts for 2 percent of global CO2 emissions (2007)
Barroso, L.A., Hölzle, U.: The Case for Energy-Proportional Computing. Computer 40, 33–37 (2007)
Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Architecture News 35, 13 (2007)
Imada, T., Sato, M., Kimura, H.: Power and QoS performance characteristics of virtualized servers. In: 2009 10th IEEE/ACM International Conference on Grid Computing, pp. 232–240 (2009)
Mei, Y., Liu, L., Pu, X., Sivathanu, S., Dong, X.: Performance analysis of network i/o workloads in virtualized data centers (2011)
Gao, Y., Guan, H., Qi, Z., Wang, B., Liu, L.: Quality of Service Aware Power Management for Virtualized Data Centers. Journal of Systems Architecture (2013)
Wang, L., Wang, H., Cai, L., Chu, R., Zhang, P., Liu, L.: A Hierarchical Memory Service Mechanism in Server Consolidation Environment. In: 2011 IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), pp. 40–47 (2011)
Anandharajan, T.R.V., Bhargavan, D., Bhagyaveni, M.A.: VM Consolidation Techniques in Cloud Datacenter. Journal of Theoretical and Applied Information Technology 53, 267–273 (2013)
Anandharajan, T.R.V., Bhagyaveni, M.A.: Co-operative Scheduled Energy Aware Load-Balancing technique for an Efficient Computational Cloud. International Journal of Computer Science Issues 8, 571–576 (2011)
Zou, H., Yuan, M.: Composite quantile regression and the oracle model selection theory. The Annals of Statistics 36, 1108–1126 (2008)
Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities. In: International Conference on High Performance Computing & Simulation, HPCS 2009, pp. 1–11 (2009)
Park, K.S., Pai, V.S.: CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Operating Systems Review 40, 65–74 (2006)
Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurrency and Computation: Practice and Experience 24(13), 1397–1420 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Venugopal Anandharajan, T.R., Bhagyaveni, M.A. (2014). Trust Based VM Consolidation in Cloud Data Centers. In: Martínez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-54525-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54524-5
Online ISBN: 978-3-642-54525-2
eBook Packages: Computer ScienceComputer Science (R0)