DEEP-SaM - Energy-Efficient Provisioning Policies for Computing Environments

The cost of electricity for datacenters is a substantial operational cost that can and should be managed, not only for saving energy, but also due to the ecologic commitment inherent to power consumption. Often, pursuing this goal results in chronic underutilization of resources, a luxury most resource providers do not have in light of their corporate commitments. This work proposes, formalizes and numerically evaluates DEEP-Sam, for clearing provisioning markets, based on the maximization of welfare, subject to utility-level dependant energy costs and customer satisfaction levels. We focus specifically on linear power models, and the implications of the inherent fixed costs related to energy consumption of modern datacenters and cloud environments. We rigorously test the model by running multiple simulation scenarios and evaluate the results critically. We conclude with positive results and implications for long-term sustainable management of modern datacenters.

[1]  Anthony A. Maciejewski,et al.  Static allocation of resources to communicating subtasks in a heterogeneous ad hoc grid environment , 2006, J. Parallel Distributed Comput..

[2]  Janet L. Wiener,et al.  Cost-aware scheduling for heterogeneous enterprise machines (CASH’EM) , 2007, 2007 IEEE International Conference on Cluster Computing.

[3]  Jordi Torres,et al.  Web Customer Modeling for Automated Session Prioritization on High Traffic Sites , 2007, User Modeling.

[4]  Karthick Rajamani,et al.  Energy Management for Commercial Servers , 2003, Computer.

[5]  Tajana Rosing,et al.  Temperature aware task scheduling in MPSoCs , 2007 .

[6]  Chandrakant D. Patel,et al.  On building next generation data centers: energy flow in the information technology stack , 2008, Bangalore Compute Conf..

[7]  Feng Pan,et al.  Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications , 2007, IEEE Transactions on Parallel and Distributed Systems.

[8]  Dirk Neumann,et al.  Trading grid services - a multi-attribute combinatorial approach , 2008, Eur. J. Oper. Res..

[9]  David Abramson,et al.  The Grid Economy , 2005, Proceedings of the IEEE.

[10]  Dirk Neumann,et al.  Economically Enhanced Resource Management for Internet Service Utilities , 2007, WISE.

[11]  Segev Wasserkrug,et al.  Autonomic self-optimization according to business objectives , 2004 .

[12]  Patrick Kurp,et al.  Green computing , 2008, Commun. ACM.

[13]  Ricardo Bianchini,et al.  Energy conservation in heterogeneous server clusters , 2005, PPoPP.

[14]  Cristina Conati,et al.  User Modeling 2007, 11th International Conference, UM 2007, Corfu, Greece, June 25-29, 2007, Proceedings , 2007, User Modeling.

[15]  Thomas F. Wenisch,et al.  PowerNap: eliminating server idle power , 2009, ASPLOS.

[16]  Rami G. Melhem,et al.  Maximizing rewards for real-time applications with energy constraints , 2003, TECS.

[17]  Boualem Benatallah.  Web Information Systems Engineering - WISE 2007, 8th International Conference on Web Information Systems Engineering, Nancy, France, December 3-7, 2007, Proceedings , 2007, WISE.

[18]  Richard Wolski,et al.  Analyzing Market-Based Resource Allocation Strategies for the Computational Grid , 2001, Int. J. High Perform. Comput. Appl..

[19]  Christoforos E. Kozyrakis,et al.  JouleSort: a balanced energy-efficiency benchmark , 2007, SIGMOD '07.

[20]  David C. Parkes,et al.  Achieving Budget-Balance with Vickrey-Based Payment Schemes in Combinatorial Exchanges , 2001 .

[21]  Enrique V. Carrera,et al.  Load balancing and unbalancing for power and performance in cluster-based systems , 2001 .

[22]  David E. Culler,et al.  User-Centric Performance Analysis of Market-Based Cluster Batch Schedulers , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[23]  Mahmut T. Kandemir,et al.  Reducing power with performance constraints for parallel sparse applications , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[24]  Cynthia Dwork,et al.  Proceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, British Columbia, Canada, May 17-20, 2008 , 2008, STOC.

[25]  Donald F. Ferguson,et al.  Economic models for allocating resources in computer systems , 1996 .

[26]  Dirk Neumann,et al.  GreedEx—a scalable clearing mechanism for utility computing , 2008, Electron. Commer. Res..

[27]  Hendrik F. Hamann.  A Measurement-Based Method for Improving Data Center Energy Efficiency , 2008, 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008).

[28]  Christoforos E. Kozyrakis,et al.  Automatic power management schemes for Internet servers and data centers , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[29]  Dror G. Feitelson,et al.  Workload Modeling for Performance Evaluation , 2002, Performance.

[30]  Prasad Raghavendra,et al.  Optimal algorithms and inapproximability results for every CSP? , 2008, STOC.

[31]  L. S. Nielsen,et al.  Low-power operation using self-timed circuits and adaptive scaling of the supply voltage , 1994, IEEE Trans. Very Large Scale Integr. Syst..

[32]  Tad Hogg,et al.  Spawn: A Distributed Computational Economy , 1992, IEEE Trans. Software Eng..

[33]  N. Carr.  IT doesn't matter , 2003, IEEE Engineering Management Review.

[34]  Richard Wolski,et al.  Eliciting honest value information in a batch-queue environment , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[35]  Jan Stallaert,et al.  A Market Design for Grid Computing , 2008, INFORMS J. Comput..

[36]  Thomas A. Corbi,et al.  The dawning of the autonomic computing era , 2003, IBM Syst. J..