Open Access Open Access  Restricted Access Subscription or Fee Access

Paper Framework for Power Aware Scheduling in Cloud Environment

Abdul Nasar Kaipancherry, Dr.K. Najeeb


Rapid growth of the demand for computational power by scientific, business and web-applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. Despite the improvements in energy efficiency of the hardware, overall energy consumption continues to grow due to increasing requirements for computing resources. The main challenge faced by cloud data centers is this power consumption by the servers. There is no proper scheduling approach for cloud providers to provide an optimal scheduling of the virtual machines (VM) to keep the power consumption minimum and to keep the Quality of Service (QoS). The proposed power aware scheduler (PASE) is based on the macro-modeling of the servers by a-priori estimation of power consumption. The a-priori estimation gives a power metric which effectively represents the relative power consumption of the servers. The ranking of the servers is based on this power metric. Here scheduling is done based on this model and there is measure for power-performance trade-off to maintain the QoS. The power aware scheduling is done by effectively scheduling the VMs by their live migration in the cloud servers based on the power ranking of the servers and also on a measure based on its CPU load.


Cloud Computing, Open Nebula, Power Aware Scheduling, Power Profile

Full Text:



Bo Li, Jianxin Li, Jinpeng Huai, Tianyu Wo, Qin Li, and Liang Zhong, “Enacloud: An energy-saving application live placement approach for cloud computing environments,” in Proc. of IEEE International Conference on Cloud Computing, 2009.

Report to Congress on Server and U.S. Environmental Protection Agency ENERGY STAR Program Data Center Energy Efficiency, “” .

Anton Beloglazov and Rajkumar Buyya, “Energy efficient resource management in virtualized cloud data centers,” in Doctoral Symposium, Proceedings of the 10th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2010), Melbourne, Australia, May 2010.

R. Buyya, C. S. Yeo, and S. Venugopal, “Market-oriented cloud computing: Vision, hype, and reality for delivering IT services as computing utilities,” in In proceedings of the 10th IEEE International conference on High Performance Computing and Communications (HPCC’08), 2008.

L. A. Barroso, U. H lzle, “The case for energy-proportional computing,” Computer, vol. 40, no. 12, pp. 33–37, 2007

R. Raghavendra, B P. Ranganathan, V. Talwar, Z. Wang, and X.Zhu, “No power struggles: Coordinated multi-level power management for the data center,” in In Thirteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS08), March 2008.

Rade Stanojevic and Robert Shorten, “Distributed dynamic speed scaling,” in Proceedings of the 29th conference on Information communications, January 2010, pp. 426–430.

Gregor von Laszewski, Lizhe Wang, Andrew J. Younge, and Xi He, “Power-aware scheduling of virtual machines in dvfs-enabled clusters,” in Proceedings of the 2009 IEEE International Conference on Cluster Computing, August 31 - September 4 2009.

G.Magklis, G. Semeraro, D. Albonesi, S. Dropsho, S. Dwarakadas, and M. Scott, “Dynamic frequency and voltage scaling for a multiple-clockdomain microprocessor,” in IEEE Micro, vol. 23, no. 6, 2003, pp. 62–68.

A. Greenberg, J. Hamilton, D .A. Maltz, and P. Patel, “The cost of a cloud: Research problems in data center networks,” ACM Computer Communications Review, vol. 39, no. 1, 2009.

W. Hammond, “Efficient power consumption in the modern datacenter. available online:,”

Sara Alspaugh, Laura Keys, and Andrew Krioukov, “Power proportional cluster,” in University of California, Berkeley, March 2010

C. Develder, M. Pickavet, B. Dhoedt, and P. Demeeste, “A power-saving strategy for grids,” in 2nd Int. Conf. on Networks for Grid Applications, Beijing, China, October 2008

Dhiman G., Marchetti G., and Rosing T.S., “vgreen: A system for energy efficient computing in virtualized environments,” in In Proceedings of the 14th IEEE/ACM International Symposium on Low Power Electronics and Design, 2009

Saurabh kumar Garg and Rajkumar Buyya, “Exploiting heterogeneity in grid computing for energy-efficient resource allocation,” in Proceedings of the 17th International Conference on Advanced Computing and Communications (ADCOM 2009), Bengaluru, India, December 2009

Liang Liu, Hao Wang, Xue Liu, Xing Jin, WenBo He, QingBo Wang, and Ying Chen, “Greencloud: a new architecture for green data center,” in International Conference on Autonomic Computing, Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, June 2009, pp. 29–38

Tridib Mukherjee, Georgios Varsamopoulos, Sandeep K. S. Gupta, and Sanjay Rungta, “Measurement-based power profiling of data center equipment,” in Proceedings of the 2007 IEEE International Conference on Cluster Computing, 2007, pp. 476–477.

Processor data sheets, “,”.

OpenNebula, “,”.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.