An experiment-driven energy consumption model for virtual machine management systems

As energy consumption is becoming critical in Cloud data centers, Cloud providers are adopting energy-efficient virtual machines management systems. These systems essentially rely on " what-if " analysis to determine what the consequence of their actions would be and to choose the best one according to a number of metrics. However, modeling energy consumption of simple operations such as starting a new VM or live-migrating is complicated by the fact that multiple phenomena occur. It is therefore important to identify which factors influence energy consumption before proposing any new model. We claim in this paper that one critical parameter is the host configuration, characterized by the number of VMs it is currently executing. Based on this observation, we present an energy model that provides energy estimation associated with VM management operations, such as VMs placement, VM start up and VM migration. The average relative estimation error is lower than 10% using the transactional web benchmark TPC-W, making it a good candidate for driving the actions of future energy-aware cloud management systems.

[1]  Romain Rouvoy,et al.  Process-level power estimation in VM-based systems , 2015, EuroSys.

[2]  Anja Strunk.  Costs of Virtual Machine Live Migration: A Survey , 2012, 2012 IEEE Eighth World Congress on Services.

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

[4]  Ada Gavrilovska,et al.  VM power metering: feasibility and challenges , 2011, PERV.

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

[6]  ProdanRadu,et al.  Modelling energy consumption of network transfers and virtual machine migration , 2016 .

[7]  Radu Prodan,et al.  Modelling energy consumption of network transfers and virtual machine migration , 2016, Future Gener. Comput. Syst..

[8]  Athanasios V. Vasilakos,et al.  Cloud Computing , 2014, ACM Comput. Surv..

[9]  Vipin Chaudhary,et al.  VMeter: Power modelling for virtualized clouds , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[10]  Feng Zhao,et al.  Virtual machine power metering and provisioning , 2010, SoCC '10.

[11]  Henry Hoffmann,et al.  Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems , 2012, TAAS.

[12]  Waltenegus Dargie,et al.  Does Live Migration of Virtual Machines Cost Energy? , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[13]  Jordi Torres,et al.  Energy accounting for shared virtualized environments under DVFS using PMC-based power models , 2012, Future Gener. Comput. Syst..

[14]  Yu Gong,et al.  Energy and performance management in large data centers: A queuing theory perspective , 2015, 2015 International Conference on Computing, Networking and Communications (ICNC).

[15]  Hai Jin,et al.  Performance and energy modeling for live migration of virtual machines , 2011, Cluster Computing.

[16]  Zoltán Ádám Mann.  Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center , 2015, Future Gener. Comput. Syst..

[17]  Xiaohong Jiang,et al.  Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[18]  OrgerieAnne-Cecile,et al.  A survey on techniques for improving the energy efficiency of large-scale distributed systems , 2014 .

[19]  Christine A. Shoemaker,et al.  Flicker: a dynamically adaptive architecture for power limited multicore systems , 2013, ISCA.

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

[21]  Gautam Kumar,et al.  CosMig: Modeling the Impact of Reconfiguration in a Cloud , 2011, 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems.

[22]  Rajkumar Buyya,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[23]  Radu Prodan,et al.  A Workload-Aware Energy Model for Virtual Machine Migration , 2015, 2015 IEEE International Conference on Cluster Computing.

[24]  Henri Casanova,et al.  Energy-aware service allocation , 2012, Future Gener. Comput. Syst..

[25]  Christine Morin,et al.  Experimental Study on the Energy Consumption in IaaS Cloud Environments , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[26]  Dmytro Dyachuk,et al.  Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[27]  HeChen,et al.  Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing , 2016 .

[28]  Alex Glikson,et al.  SLA-aware resource over-commit in an IaaS cloud , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

[29]  Guofeng Zhu,et al.  Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing , 2015, Computing.

[30]  Long Wang,et al.  Towards an Understanding of Oversubscription in Cloud , 2012, Hot-ICE.

[31]  Kresimir Mihic,et al.  A system for online power prediction in virtualized environments using gaussian mixture models , 2010, Design Automation Conference.

[32]  Alexander Schill,et al.  Power Consumption Estimation Models for Processors, Virtual Machines, and Servers , 2014, IEEE Transactions on Parallel and Distributed Systems.

[33]  Shivakant Mishra,et al.  Modeling CPU energy consumption for energy efficient scheduling , 2010, GCM '10.

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

[35]  Gurindar S. Sohi,et al.  Holistic run-time parallelism management for time and energy efficiency , 2013, ICS '13.

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

[37]  Raffaela Mirandola,et al.  A Bio-inspired Algorithm for Energy Optimization in a Self-organizing Data Center , 2009, SOAR.

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

[39]  Petter Svärd,et al.  Principles and Performance Characteristics of Algorithms for Live VM Migration , 2015, OPSR.

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

[41]  Anja Strunk,et al.  A Lightweight Model for Estimating Energy Cost of Live Migration of Virtual Machines , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[42]  Mor Harchol-Balter,et al.  Optimality analysis of energy-performance trade-off for server farm management , 2010, Perform. Evaluation.

[43]  Henry Hoffmann,et al.  A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints , 2015, ASPLOS.