Reducing energy consumption in distributed computing through economic resource allocation

Energy consumption is an increasingly important consideration in computing. High-performance computing environments consume substantial amounts of energy and the cost of energy is increasing. We explore the possibility of reducing the energy consumption of a grid of heterogeneous computers through appropriate resource allocation strategies. We examine a number of possible grid workload scenarios and analyse the impact of different resource allocation mechanisms on energy consumption and time taken to execute tasks. We perform this analysis first on a cluster of heterogeneous nodes and then scale up the experiment to a grid of multiple clusters. Our results show that different resource allocation mechanisms perform better under different scenarios, and that selection of the resource allocation mechanism can significantly alter grid energy consumption.

[1]  Wu-chun Feng,et al.  A Power-Aware Run-Time System for High-Performance Computing , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[2]  Dhananjay K. Gode,et al.  Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality , 1993, Journal of Political Economy.

[3]  Amin Vahdat,et al.  Every joule is precious: the case for revisiting operating system design for energy efficiency , 2000, ACM SIGOPS European Workshop.

[4]  Samee Ullah Khan,et al.  A Game Theoretical Energy Efficient Resource Allocation Technique for Large Distributed Computing Systems , 2009, PDPTA.

[5]  Hesham El-Rewini,et al.  Power Aware Scheduling in Computational Grids , 2009, PDPTA.

[6]  Claudio Scordino,et al.  Energy-Efficient Real-Time Heterogeneous Server Clusters , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).

[7]  Toby Velte,et al.  Green IT: Reduce Your Information System's Environmental Impact While Adding to the Bottom Line , 2008 .

[8]  Akshat Verma,et al.  Power-aware dynamic placement of HPC applications , 2008, ICS '08.

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

[10]  Pascal Bouvry,et al.  A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids , 2012, Cluster Computing.

[11]  Juan Li,et al.  Comparison and analysis of eight scheduling heuristics for the optimization of energy consumption and makespan in large-scale distributed systems , 2010, The Journal of Supercomputing.

[12]  Yukikazu Nakamoto,et al.  Power-Aware Resource Allocation with Fair QoS Guarantee , 2006, 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'06).

[13]  Albert Y. Zomaya,et al.  Efficient Hierarchical Task Scheduling on GRIDS Accounting for Computation and Communications , 2011, Intelligent Decision Systems in Large-Scale Distributed Environments.

[14]  Chandrakant D. Patel,et al.  Energy Aware Grid: Global Workload Placement Based on Energy Efficiency , 2003 .

[15]  Wu-chun Feng,et al.  Towards efficient supercomputing: a quest for the right metric , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[16]  A.J. Shah,et al.  Optimization of Global Data Center Thermal Management Workload for Minimal Environmental and Economic Burden , 2008, IEEE Transactions on Components and Packaging Technologies.

[17]  David K. Lowenthal,et al.  Minimizing execution time in MPI programs on an energy-constrained, power-scalable cluster , 2006, PPoPP '06.

[18]  Feng Pan,et al.  Exploring the energy-time tradeoff in MPI programs on a power-scalable cluster , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[19]  Pascal Bouvry,et al.  Memetic Algorithms for Energy-Aware Computation and Communications Optimization in Computing Clusters , 2012, Handbook of Energy-Aware and Green Computing.

[20]  Simon,et al.  Resource allocation to conserve energy in distributed computing , 2011, Int. J. Grid Util. Comput..

[21]  Wu-chun Feng,et al.  A Feasibility Analysis of Power Awareness in Commodity-Based High-Performance Clusters , 2005, 2005 IEEE International Conference on Cluster Computing.

[22]  Albert Y. Zomaya,et al.  Cooperative power-aware scheduling in grid computing environments , 2010, J. Parallel Distributed Comput..

[23]  Salim Hariri,et al.  Autonomic power and performance management for computing systems , 2006, 2006 IEEE International Conference on Autonomic Computing.

[24]  Pascal Bouvry,et al.  Energy-aware fast scheduling heuristics in heterogeneous computing systems , 2011, 2011 International Conference on High Performance Computing & Simulation.

[25]  Laurent Lefèvre,et al.  The GREEN-NET framework: Energy efficiency in large scale distributed systems , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

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