Electric grid balancing through lowcost workload migration

Energy production must continuously match demand on the electric grid. A deficiency can lead to service disruptions, and a surplus can place tremendous stress on grid components, potentially causing major blackouts. To manage this balance, grid operators must increase or lower power generation, with only a few minutes to react. The grid balancing problem has also impeded the pace of integrating bountiful renewable resources (e.g., wind), whose generation is intermittent. An emerging plan to mitigate this problem is demand response, i.e., for grid operators to alter the electricity usage behavior of the masses through real-time price signals. But due to prohibitively high infrastructure costs and societal-scale adoption, tangible demand response mechanisms have so far been elusive. We believe that altering the usage patterns of a multitude of data centers can be a tangible, albeit initial, step towards affecting demand response. Growing in both density and size, today's data center designs are shaped by the increasing awareness of energy costs and carbon footprint. We posit that shifting computational workloads (and thus, demand) across geographic regions to match electricity supply may help balance the grid. In this paper we will first present a real grid balancing problem experienced in the Pacfic Northwest. We then propose a symbiotic relationship between data centers and grid operators by showing that mutual cost benefits can be accessible. Finally, we argue for a low cost workload migration mechanism, and pose overarching challenges in designing this framework.

[1]  WiermanAdam,et al.  Renewable and cooling aware workload management for sustainable data centers , 2012 .

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

[3]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[4]  Vipin Chaudhary,et al.  VMeter: Power modelling for virtualized clouds , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[5]  Alexander S. Szalay,et al.  Low-power amdahl-balanced blades for data intensive computing , 2010, OPSR.

[6]  Xue Liu,et al.  Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment , 2010, 2010 Proceedings IEEE INFOCOM.

[7]  J. Koomey Worldwide electricity used in data centers , 2008 .

[8]  Adam Wierman,et al.  Renewable and cooling aware workload management for sustainable data centers , 2012, SIGMETRICS '12.

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

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

[11]  Jordi Torres,et al.  GreenSlot: Scheduling energy consumption in green datacenters , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[12]  Thu D. Nguyen,et al.  Reducing electricity cost through virtual machine placement in high performance computing clouds , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

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

[14]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[15]  Thu D. Nguyen,et al.  Cost-and Energy-Aware Load Distribution Across Data Centers , 2009 .

[16]  Christopher Stewart,et al.  Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter∗ , 2009 .

[17]  Christopher Stewart,et al.  Policy and mechanism for carbon-aware cloud applications , 2012, 2012 IEEE Network Operations and Management Symposium.

[18]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[19]  Andy Hopper,et al.  Free Lunch: Exploiting Renewable Energy for Computing , 2011, HotOS.

[20]  Karsten Schwan,et al.  VirtualPower: coordinated power management in virtualized enterprise systems , 2007, SOSP.

[21]  Chenyu Wang,et al.  Exploring MapReduce efficiency with highly-distributed data , 2011, MapReduce '11.

[22]  Liang Liu,et al.  GreenCloud: a new architecture for green data center , 2009, ICAC-INDST '09.