GGreen: A Greedy Energy-Aware Scheduling Algorithm on Grid Systems

Current large scale distributed systems face growth in data center electricity. In this paper, we have discussed energy-aware scheduling problem on grid systems. In this way, we provide GGreen -- a greedy task scheduling algorithm in order to reduce the global energy consumption on grid systems. GGreen strategy has an observational approach that focus on assigning heavy tasks to lowest power resource. Additionally, GGreen algorithm has been implemented and evaluated by using a grid simulation environment over three distinct scenarios. Experimental results show gains as compared to other alternatives with the main benefit of reducing the energy consumption.

[1]  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.

[2]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[3]  Kenli Li,et al.  Energy-aware task scheduling in heterogeneous computing environments , 2014, Cluster Computing.

[4]  Jack Dongarra,et al.  Scheduling in the Grid application development software project , 2004 .

[5]  Rajkumar Buyya,et al.  A taxonomy and survey of grid resource management systems for distributed computing , 2002, Softw. Pract. Exp..

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

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

[8]  Pascal Bouvry,et al.  Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems , 2013, Journal of Grid Computing.

[9]  Albert Y. Zomaya,et al.  Linear Combinations of DVFS-Enabled Processor Frequencies to Modify the Energy-Aware Scheduling Algorithms , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[10]  Laurent Lefèvre,et al.  Designing and evaluating an energy efficient Cloud , 2010, The Journal of Supercomputing.

[11]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[12]  Fábio Coutinho,et al.  A Workflow Scheduling Algorithm for Optimizing Energy-Efficient Grid Resources Usage , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.

[13]  Valentin Cristea,et al.  Optimizing the Energy Efficiency of Message Exchanging for Service Distribution in Interoperable Infrastructures , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.

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

[15]  Jin Suk Kim,et al.  An Online Scheduling Algorithm for Grid Computing Systems , 2003, GCC.

[16]  Valentin Cristea,et al.  Using a novel message-exchanging optimization (MEO) model to reduce energy consumption in distributed systems , 2013, Simul. Model. Pract. Theory.

[17]  Rajkumar Buyya,et al.  Adaptive workflow scheduling for dynamic grid and cloud computing environment , 2013, Concurr. Comput. Pract. Exp..

[18]  Sarbjeet Singh,et al.  A Survey of Workflow Scheduling Algorithms and Research Issues , 2013 .

[19]  Gregor von Laszewski,et al.  Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[20]  Chia-Ming Wu,et al.  A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters , 2014, Future Gener. Comput. Syst..

[21]  Bernhard Schott,et al.  Scalable Computing: Practice and Experience , 2011 .

[22]  Andrei Tchernykh,et al.  Adaptive energy efficient scheduling in Peer-to-Peer desktop grids , 2014, Future Gener. Comput. Syst..

[23]  Claudio T. Bornstein,et al.  A Scheduling Strategy for Global Scientific Grids - Minimizing Simultaneously Time and Energy Consumption , 2013, ICEIS.

[24]  Jordi Torres,et al.  GreenHadoop: leveraging green energy in data-processing frameworks , 2012, EuroSys '12.

[25]  San Murugesan,et al.  Harnessing Green IT: Principles and Practices , 2008, IT Professional.

[26]  Tajana Rosing,et al.  Analysis of dynamic voltage scaling for system level energy management , 2008, CLUSTER 2008.