Resource allocation in a Cloud partially powered by renewable energy sources. (Allocation de ressources dans un Cloud partiellement alimenté par des sources d'énergie renouvelable)

Most of the energy-efficient Cloud frameworks proposed in literature do not consider electricity availability and renewable energy in their models. Integrating renewable energy into data centers significantly reduces the traditional energy consumption and carbon footprint of these energy-hungry infrastructures. As renewable energy is intermittent and fluctuates with time-varying, it is usually under-utilized. We address the problem of improving the utilization of renewable energy for a single data center and investigate two approaches: opportunistic scheduling and energy storage. Our results demonstrate that both approaches are able to reduce the brown energy consumption under different configurations. We extend this work to the context of Edge Clouds and Internet of Things on the use case of data stream analysis. We show how to make Edge Clouds greener with on-site renewable energy production combined with energy storage and performance degradation of the users’ applications.

[1]  Gang Qu,et al.  Approaching the Maximum Energy Saving on Embedded Systems with Multiple Voltages , 2003, ICCAD 2003.

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

[3]  Cristian Mateos,et al.  Dynamic Scheduling based on Particle Swarm Optimization for Cloud-based Scientific Experiments , 2014, CLEI Electron. J..

[4]  Ioannis Tomkos,et al.  A Survey on Optical Interconnects for Data Centers , 2012, IEEE Communications Surveys & Tutorials.

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

[6]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

[7]  Laurent Lefèvre,et al.  Save Watts in Your Grid: Green Strategies for Energy-Aware Framework in Large Scale Distributed Systems , 2008, 2008 14th IEEE International Conference on Parallel and Distributed Systems.

[8]  Eric Bouillet,et al.  Efficient resource provisioning in compute clouds via VM multiplexing , 2010, ICAC '10.

[9]  Weisong Shi,et al.  FlexFetch: A History-Aware Scheme for I/O Energy Saving in Mobile Computing , 2007, 2007 International Conference on Parallel Processing (ICPP 2007).

[10]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[11]  Yuping Wang,et al.  Energy-Efficient Multi-Job Scheduling Model for Cloud Computing and Its Genetic Algorithm , 2012 .

[12]  Sanjeev Khanna,et al.  On multi-dimensional packing problems , 2004, SODA '99.

[13]  Tansu Alpcan,et al.  Fog Computing May Help to Save Energy in Cloud Computing , 2016, IEEE Journal on Selected Areas in Communications.

[14]  Laurent Lefèvre,et al.  Towards Generalizing "Big Little" for Energy Proportional HPC and Cloud Infrastructures , 2014, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.

[15]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[16]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[17]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

[18]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[19]  Manish Parashar,et al.  Enabling autonomic computing on federated advanced cyberinfrastructures , 2013, CAC.

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

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

[22]  Arif Merchant,et al.  Flash Reliability in Production: The Expected and the Unexpected , 2016, FAST.

[23]  Jean-Marc Menaud,et al.  Estimating the Power Consumption of an Idle Virtual Machine , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[24]  Lachlan L. H. Andrew,et al.  Greening Geographical Load Balancing , 2015, IEEE/ACM Transactions on Networking.

[25]  Jean-Marc Pierson,et al.  Spatio-temporal thermal-aware scheduling for homogeneous high-performance computing datacenters , 2017, Future Gener. Comput. Syst..

[26]  Albert Y. Zomaya,et al.  A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems , 2010, Adv. Comput..

[27]  THE CARBON EMISSIONS OF SERVER COMPUTING FOR SMALL- TO MEDIUM-SIZED ORGANIZATIONS , 2012 .

[28]  Yacine Rezgui,et al.  In-Transit Data Analysis and Distribution in a Multi-cloud Environment Using CometCloud , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[29]  Mahadev Satyanarayanan,et al.  Scalable crowd-sourcing of video from mobile devices , 2013, MobiSys '13.

[30]  M. Yue A simple proof of the inequality FFD (L) ≤ 11/9 OPT (L) + 1, ∀L for the FFD bin-packing algorithm , 1991 .

[31]  Laurent Lefèvre,et al.  "Big, Medium, Little": Reaching Energy Proportionality with Heterogeneous Computing Scheduler , 2015, Parallel Process. Lett..

[32]  Barry O'Sullivan,et al.  Trends in Constraint Programming , 2007 .

[33]  Thu D. Nguyen,et al.  Providing green SLAs in High Performance Computing clouds , 2013, 2013 International Green Computing Conference Proceedings.

[34]  TangMaolin,et al.  A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers , 2015 .

[35]  Florin Ciucu,et al.  A Stochastic Power Network Calculus for Integrating Renewable Energy Sources into the Power Grid , 2012, IEEE Journal on Selected Areas in Communications.

[36]  Hiroto Yasuura,et al.  Voltage scheduling problem for dynamically variable voltage processors , 1998, Proceedings. 1998 International Symposium on Low Power Electronics and Design (IEEE Cat. No.98TH8379).

[37]  Martin Bichler,et al.  A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers , 2010, IEEE Transactions on Services Computing.

[38]  Toyotaro Suzumura,et al.  Elastic Stream Computing with Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[39]  Giuseppe Serazzi,et al.  Stochastic Analysis of Energy Consumption in Pool Depletion Systems , 2016, MMB/DFT.

[40]  Peter Kilpatrick,et al.  Challenges and Opportunities in Edge Computing , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[41]  Thomas Ledoux,et al.  Towards energy-proportional clouds partially powered by renewable energy , 2016, Computing.

[42]  Shangguang Wang,et al.  An overview of Internet of Vehicles , 2014, China Communications.

[43]  Wentong Cai,et al.  Dynamic Bin Packing for On-Demand Cloud Resource Allocation , 2016, IEEE Transactions on Parallel and Distributed Systems.

[44]  Philippe Gravey,et al.  112-Gbit/s Passive Optical Pod Interconnect for small data centers using Pulse Amplitude Modulation , 2016, 2016 International Conference on Optical Network Design and Modeling (ONDM).

[45]  Dan Xu,et al.  Geographic trough filling for internet datacenters , 2011, 2012 Proceedings IEEE INFOCOM.

[46]  Christian Belady,et al.  GREEN GRID DATA CENTER POWER EFFICIENCY METRICS: PUE AND DCIE , 2008 .

[47]  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.

[48]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[49]  Gang Quan,et al.  Energy efficient fixed-priority scheduling for real-time systems on variable voltage processors , 2001, DAC '01.

[50]  Martin Bichler,et al.  Capacity Planning for Virtualized Servers , 2007 .

[51]  Mohsen Guizani,et al.  Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment , 2015, IEEE Network.

[52]  Bu-Sung Lee,et al.  Optimal Power Management for Server Farm to Support Green Computing , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[53]  Ohad Shamir,et al.  On-demand, Spot, or Both: Dynamic Resource Allocation for Executing Batch Jobs in the Cloud , 2014, ICAC.

[54]  Emmanuel Jeannot,et al.  Adding Virtualization Capabilities to the Grid'5000 Testbed , 2012, CLOSER.

[55]  Gargi Dasgupta,et al.  Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.

[56]  Athanasios V. Vasilakos,et al.  Thermal-Aware Scheduling of Batch Jobs in Geographically Distributed Data Centers , 2014, IEEE Transactions on Cloud Computing.

[57]  Yuan Yao,et al.  Data centers power reduction: A two time scale approach for delay tolerant workloads , 2012, 2012 Proceedings IEEE INFOCOM.

[58]  L. Benini,et al.  System-level dynamic power management , 1999, Proceedings IEEE Alessandro Volta Memorial Workshop on Low-Power Design.

[59]  Bu-Sung Lee,et al.  Optimal virtual machine placement across multiple cloud providers , 2009, 2009 IEEE Asia-Pacific Services Computing Conference (APSCC).

[60]  Yanpei Chen,et al.  Integrating Renewable Energy Using Data Analytics Systems: Challenges and Opportunities , 2011, IEEE Data Eng. Bull..

[61]  Yanpei Chen,et al.  An information-centric energy infrastructure: The Berkeley view , 2011, Sustain. Comput. Informatics Syst..

[62]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[63]  Jean-Marc Menaud,et al.  Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[64]  Sergiu Nedevschi,et al.  Reducing Network Energy Consumption via Sleeping and Rate-Adaptation , 2008, NSDI.

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

[66]  Rajkumar Buyya,et al.  Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers , 2013, Euro-Par.

[67]  Albert Y. Zomaya,et al.  Some observations on optimal frequency selection in DVFS-based energy consumption minimization , 2011, J. Parallel Distributed Comput..

[68]  Vijay K. Naik,et al.  Biting Off Safely More Than You Can Chew: Predictive Analytics for Resource Over-Commit in IaaS Cloud , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[69]  Samuli Aalto,et al.  Energy-Aware Server with SRPT Scheduling: Analysis and Optimization , 2016, QEST.

[70]  Xiangliang Zhang,et al.  Virtual machine migration in an over-committed cloud , 2012, 2012 IEEE Network Operations and Management Symposium.

[71]  F. Frances Yao,et al.  A scheduling model for reduced CPU energy , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

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

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

[74]  Marvin Theimer,et al.  Preemptable remote execution facilities for the V-system , 1985, SOSP '85.

[75]  Catherine Rosenberg,et al.  Toward a Realistic Performance Analysis of Storage Systems in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[76]  Wei Li,et al.  Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm , 2012, ICONIP.

[77]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[78]  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.

[79]  Muli Ben-Yehuda,et al.  Ginkgo : Automated , Application-Driven Memory Overcommitment for Cloud Computing , 2011 .

[80]  Bill Tschudi,et al.  ERE: A METRIC FOR MEASURING THE BENEFIT OF REUSE ENERGY FROM A DATA CENTER , 2010 .

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

[82]  Xue Liu,et al.  Coordinated Energy Cost Management of Distributed Internet Data Centers in Smart Grid , 2012, IEEE Transactions on Smart Grid.

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

[84]  Xavier Lorca,et al.  Entropy: a consolidation manager for clusters , 2009, VEE '09.

[85]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[86]  Mohsen Guizani,et al.  Efficient datacenter resource utilization through cloud resource overcommitment , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[87]  Antonio Luque,et al.  Handbook of photovoltaic science and engineering , 2011 .

[88]  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.

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

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

[91]  Thu D. Nguyen,et al.  Parasol and GreenSwitch: managing datacenters powered by renewable energy , 2013, ASPLOS '13.

[92]  Jean-Marc Menaud,et al.  Balancing the Use of Batteries and Opportunistic Scheduling Policies for Maximizing Renewable Energy Consumption in a Cloud Data Center , 2017, 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP).

[93]  Allan Borodin,et al.  Online computation and competitive analysis , 1998 .

[94]  Umesh Deshpande,et al.  Inter-rack live migration of multiple virtual machines , 2012, VTDC '12.

[95]  Xiaorui Wang,et al.  Server-Level Power Control , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[96]  Martin Suchara,et al.  Greening backbone networks: reducing energy consumption by shutting off cables in bundled links , 2010, Green Networking '10.

[97]  Yuguang Fang,et al.  Cutting Down Electricity Cost in Internet Data Centers by Using Energy Storage , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[98]  Ravi Sunil,et al.  ENABLING SMART CLOUD SERVICES THROUGH REMOTE SENSING: AN INTERNET OF EVERYTHING ENABLER , 2015 .

[99]  Wentong Cai,et al.  On dynamic bin packing for resource allocation in the cloud , 2014, SPAA.

[100]  Jihong Kim,et al.  Power-Aware Resource Management Techniques for Low-Power Embedded Systems , 2007, Handbook of Real-Time and Embedded Systems.

[101]  Mor Harchol-Balter,et al.  Optimal power allocation in server farms , 2009, SIGMETRICS '09.

[102]  Yefu Wang,et al.  GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy , 2011, Middleware.

[103]  Enzo Baccarelli,et al.  Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study , 2016, IEEE Network.

[104]  Nick Antonopoulos,et al.  Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics , 2019, IEEE Transactions on Cloud Computing.

[105]  Roi Blanco,et al.  Exploiting Green Energy to Reduce the Operational Costs of Multi-Center Web Search Engines , 2016, WWW.

[106]  Jaume Salom,et al.  Energy-efficient, thermal-aware modeling and simulation of data centers: The CoolEmAll approach and evaluation results , 2015, Ad Hoc Networks.

[107]  Alec Brooks,et al.  Demand Dispatch - Using Real-Time Control of Demand to help Balance Generation and Load , 2010 .

[108]  Christine Morin,et al.  Energy-Aware Ant Colony Based Workload Placement in Clouds , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[109]  Wentong Cai,et al.  Competitiveness of Dynamic Bin Packing for Online Cloud Server Allocation , 2017, IEEE/ACM Transactions on Networking.

[110]  Meeta Sharma Gupta,et al.  System level analysis of fast, per-core DVFS using on-chip switching regulators , 2008, 2008 IEEE 14th International Symposium on High Performance Computer Architecture.

[111]  Prashant J. Shenoy,et al.  Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[112]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[113]  Gilles Fedak,et al.  Beyond The Cloud, How Should Next Generation Utility Computing Infrastructures Be Designed? , 2013 .

[114]  Bingsheng He,et al.  Green-aware workload scheduling in geographically distributed data centers , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[115]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[116]  Chong Luo,et al.  Multimedia Cloud Computing , 2011, IEEE Signal Processing Magazine.

[117]  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.

[118]  Qiang Huang,et al.  Power Consumption of Virtual Machine Live Migration in Clouds , 2011, 2011 Third International Conference on Communications and Mobile Computing.