A Stochastic Approach for Optimizing Green Energy Consumption in Distributed Clouds

The energy drawn by Cloud data centers is reaching worrying levels, thus inciting providers to install on-site green energy producers, such as photovoltaic panels. Considering distributed Clouds, workload managers need to geographically allocate virtual machines according to the green production in order not to waste energy. In this paper, we propose SAGITTA: a Stochastic Approach for Green consumption In disTributed daTA centers. We show that compared to the optimal solution, SAGITTA consumes 4% more brown energy, and wastes only 3.14% of the available green energy, while a traditional round-robin solution consumes 14.4% more energy overall than optimum, and wastes 28.83% of the available green energy.

[1]  Guillaume Pierre,et al.  An experiment-driven energy consumption model for virtual machine management systems , 2018, Sustain. Comput. Informatics Syst..

[2]  Hanan Lutfiyya,et al.  DCSim: A data centre simulation tool for evaluating dynamic virtualized resource management , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

[3]  Rakesh Tripathi,et al.  Optimizing Green Energy, Cost, and Availability in Distributed Data Centers , 2017, IEEE Communications Letters.

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

[5]  Laurent Ciarletta,et al.  Hybrid Co-simulation of FMUs using DEV&DESS in MECSYCO , 2016, 2016 Symposium on Theory of Modeling and Simulation (TMS-DEVS).

[6]  Laurent Ciarletta,et al.  MECSYCO: a Multi-agent DEVS Wrapping Platform for the Co-simulation of Complex Systems , 2016 .

[7]  Lizhe Wang,et al.  Scientific Cloud Computing: Early Definition and Experience , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

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

[9]  Richard Talaber,et al.  USING VIRTUALIZATION TO IMPROVE DATA CENTER EFFICIENCY , 2009 .

[10]  David C. Snowdon,et al.  Power Management and Dynamic Voltage Scaling: Myths and Facts , 2005 .

[11]  Ragunathan Rajkumar,et al.  Critical power slope: understanding the runtime effects of frequency scaling , 2002, ICS '02.

[12]  M. Savoie,et al.  Converged Optical Network Infrastructures in Support of Future Internet and Grid Services Using IaaS to Reduce GHG Emissions , 2009, Journal of Lightwave Technology.

[13]  Anand Sivasubramaniam,et al.  Energy storage in datacenters: what, where, and how much? , 2012, SIGMETRICS '12.

[14]  Anne-Cécile Orgerie,et al.  Impact of Shutdown Techniques for Energy-Efficient Cloud Data Centers , 2016, ICA3PP.

[15]  Richard E. Brown,et al.  United States Data Center Energy Usage Report , 2016 .

[16]  Bo Li,et al.  Harnessing renewable energy in cloud datacenters: opportunities and challenges , 2014, IEEE Network.

[17]  Shaolei Ren,et al.  Provably-Efficient Job Scheduling for Energy and Fairness in Geographically Distributed Data Centers , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[18]  Jean-Marc Menaud,et al.  Opportunistic Scheduling in Clouds Partially Powered by Green Energy , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[19]  R.H. Katz,et al.  Tech Titans Building Boom , 2009, IEEE Spectrum.

[20]  Bernard P. Zeigler,et al.  Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems , 2000 .