Shutdown Policies with Power Capping for Large Scale Computing Systems

Large scale distributed systems are expected to consume huge amounts of energy. To solve this issue, shutdown policies constitute an appealing approach able to dynamically adapt the resource set to the actual workload. However, multiple constraints have to be taken into account for such policies to be applied on real infrastructures, in particular the time and energy cost of shutting down and waking up nodes, and power capping to avoid disruption of the system. In this paper, we propose models translating these various constraints into different shutdown policies that can be combined. Our models are validated through simulations on real workload traces and power measurements on real testbeds.

[1]  Martin Schulz,et al.  Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[2]  Yonggang Wen,et al.  Towards Joint Optimization Over ICT and Cooling Systems in Data Centre: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[3]  Franck Cappello,et al.  Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed , 2006, Int. J. High Perform. Comput. Appl..

[4]  Andy B. Yoo,et al.  Approved for Public Release; Further Dissemination Unlimited X-ray Pulse Compression Using Strained Crystals X-ray Pulse Compression Using Strained Crystals , 2002 .

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

[6]  Morteza Zadimoghaddam,et al.  Scheduling to minimize gaps and power consumption , 2013, Journal of Scheduling.

[7]  Sherief Reda,et al.  Techniques for energy-efficient power budgeting in data centers , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[8]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[9]  Jerome A. Rolia,et al.  Resource pool management: Reactive versus proactive or let's be friends , 2009, Comput. Networks.

[10]  ANNE-CECILE ORGERIE,et al.  Eridis: Energy-Efficient Reservation Infrastructure for Large-Scale Distributed Systems , 2011, Parallel Process. Lett..

[11]  Laurent Lefèvre,et al.  Save Watts in Your Grid: Green Strategies for Energy-Aware Framework in Large Scale Distributed Systems , 2008, 2008 14th IEEE International Conference on Parallel and Distributed Systems.

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

[13]  Ulas C. Kozat,et al.  Dynamic resource allocation and power management in virtualized data centers , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[14]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[15]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[16]  Karthikeyan Sankaralingam,et al.  Power Limitations and Dark Silicon Challenge the Future of Multicore , 2012, TOCS.

[17]  Jia Liu,et al.  Setting Energy Efficiency Goals in Data Centers: The GAMES Approach , 2012, E2DC.

[18]  Mor Harchol-Balter,et al.  Optimality analysis of energy-performance trade-off for server farm management , 2010, Perform. Evaluation.