Pricing and peak aware scheduling algorithm for cloud computing

The proposed cloud computing scheduling algorithms demonstrated feasibility of interactions between distributors and one of their heavy use customers in a smart grid environment. Specifically, the proposed algorithms take cues from the dynamic pricing and schedule the jobs/tasks in ways that the energy usage is what distributors are hinted. In addition, a peak threshold can be dynamically assigned such that the energy usage at any given time will not exceed the threshold. The proposed scheduling algorithm proved the feasibility of managing the energy usage of cloud computers in collaboration with the energy distributor.

[1]  Rajkumar Buyya,et al.  A toolkit for modelling and simulating data Grids: an extension to GridSim , 2008, Concurr. Comput. Pract. Exp..

[2]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[3]  Kang G. Shin,et al.  Real-time dynamic voltage scaling for low-power embedded operating systems , 2001, SOSP.

[4]  Erol Gelenbe,et al.  Energy-Efficient Cloud Computing , 2010, Comput. J..

[5]  Floriano Zini,et al.  Evaluation of an economy-based file replication strategy for a data grid , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[6]  Sebnem Rusitschka,et al.  Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[7]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[8]  Mario J. Gonzalez.  Deterministic Processor Scheduling , 1977, CSUR.

[9]  Amir-Hamed Mohsenian-Rad,et al.  Coordination of Cloud Computing and Smart Power Grids , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[10]  Ahmad Faruqui,et al.  The Power of Dynamic Pricing , 2009 .

[11]  Sumit Kumar Bose,et al.  Leveraging Smart-Meters for Initiating Application Migration across Clouds for Performance and Power-Expenditure Trade-Offs , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[12]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[13]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[14]  Y.-K. Kwok,et al.  Static scheduling algorithms for allocating directed task graphs to multiprocessors , 1999, CSUR.

[15]  Frank D. Anger,et al.  Scheduling Precedence Graphs in Systems with Interprocessor Communication Times , 1989, SIAM J. Comput..

[16]  Rodney S. Tucker,et al.  Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport , 2011, Proceedings of the IEEE.

[17]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[18]  Ian Lumb,et al.  A Taxonomy and Survey of Cloud Computing Systems , 2009, 2009 Fifth International Joint Conference on INC, IMS and IDC.

[19]  Karthick Rajamani,et al.  Designing Energy-Efficient Servers and Data Centers , 2010, Computer.

[20]  Jonathan Wang,et al.  Case studies of smart grid demand response programs in North America , 2011, ISGT 2011.

[21]  K. Mani Chandy,et al.  A comparison of list schedules for parallel processing systems , 1974, Commun. ACM.

[22]  Richard E. Brown,et al.  Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431 , 2008 .