Analyzing the Effects of Load Distribution Algorithms on Energy Consumption of Servers in Cloud Data Centers

Cloud computing has become an important driver for IT service provisioning in recent years. It offers additional flexibility to both customers and IT service providers, but also comes along with new challenges for providers. One of the major challenges for providers is the reduction of energy consumption since today, already more than 50% of operational costs in data centers account for energy. A possible way to reduce these costs is to efficiently distribute load within the data center. Although the effect of load distribution algorithms on energy consumption is a topic of recent research, an analysis-framework for evaluating arbitrary load distribution algorithms with regard to their effects on the energy consumption of cloud data centers is still nonexistent. Therefore, in this contribution, a concept of a simulation-based, quantitative analysisframework for load distribution algorithms in cloud environments with respect to the energy consumption of data centers is developed and evaluated.

[1]  Marius Hillenbrand,et al.  High performance cloud computing , 2013, Future Gener. Comput. Syst..

[2]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[3]  Henri Casanova,et al.  SimGrid: A Generic Framework for Large-Scale Distributed Experiments , 2008, Tenth International Conference on Computer Modeling and Simulation (uksim 2008).

[4]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

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

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

[7]  Georgia Sakellari,et al.  A survey of mathematical models, simulation approaches and testbeds used for research in cloud computing , 2013, Simul. Model. Pract. Theory.

[8]  Christoforos E. Kozyrakis,et al.  A Comparison of High-Level Full-System Power Models , 2008, HotPower.

[9]  Jack P. C. Kleijnen,et al.  EUROPEAN JOURNAL OF OPERATIONAL , 1992 .

[10]  Laurent Lefèvre,et al.  Demystifying energy consumption in Grids and Clouds , 2010, International Conference on Green Computing.

[11]  Jeffrey Rambo,et al.  Modeling of data center airflow and heat transfer: State of the art and future trends , 2007, Distributed and Parallel Databases.

[12]  Jesús Carretero,et al.  iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator , 2012, Journal of Grid Computing.

[13]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[14]  Ayan Banerjee,et al.  GDCSim: A tool for analyzing Green Data Center design and resource management techniques , 2011, 2011 International Green Computing Conference and Workshops.

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

[16]  Rajkumar Buyya,et al.  Dynamically scaling applications in the cloud , 2011, CCRV.

[17]  Sascha Bosse,et al.  Analysis of simulation tools for determining the energy consumption of data centers for cloud computing , 2014 .

[18]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[19]  Chita R. Das,et al.  MDCSim: A multi-tier data center simulation, platform , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

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

[21]  Richard E. Brown,et al.  Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431 , 2008 .

[22]  Dzmitry Kliazovich,et al.  GreenCloud: a packet-level simulator of energy-aware cloud computing data centers , 2010, The Journal of Supercomputing.

[23]  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.

[24]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition , 2013, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition.

[25]  JENS GUSTEDT,et al.  Experimental Methodologies for Large-Scale Systems: a Survey , 2009, Parallel Process. Lett..

[26]  Christoph Meinel,et al.  Online assessment for hands-on cyber security training in a virtual lab , 2012, Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON).

[27]  Laurent Lefèvre,et al.  A survey on techniques for improving the energy efficiency of large-scale distributed systems , 2014, ACM Comput. Surv..

[28]  Xiao Chen,et al.  Distributed System Design , 2000, Scalable Comput. Pract. Exp..

[29]  Henri Casanova,et al.  On the validity of flow-level tcp network models for grid and cloud simulations , 2013, TOMC.

[30]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[31]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

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

[33]  Dzmitry Kliazovich,et al.  GreenCloud: A Packet-Level Simulator of Energy-Aware Cloud Computing Data Centers , 2010, GLOBECOM.