The CiS2: a new metric for performance and energy trade-off in consolidated servers

The increased use of cloud services has turned the virtualization in the main technology that supports cloud datacentres. To reduce the increment of power consumption caused in datacenters due to the addition of physical servers, system administrators are using virtual machine consolidation (VMC) techniques which tries to allocate the adequate number of virtual machines per physical server. Therefore, VMC increases server resources utilization and as a consequence its performance degradation and the energy consumption too. Then, a trade-off between the performance and the energy consumption exists when consolidating virtual machines. This trade-off is difficult to quantify and also to determine the servers efficiency taking into account a specific number of allocated virtual machines. Because of this, it is crucial for system administrators having a simple metric that assists the VMC making-decision process. In this paper, we propose the $$CiS^2$$ index, a metric to quantify this performance-energy trade-off. Also, this index can help system administrators to decide about the servers’ efficiency through benchmarking and to select the most efficient server through a proposed algorithm. Besides, we propose a simple graphical representation of the index to distinguish graphically the efficient and non-efficient server consolidations. We validate the index in a theoretical manner and performing real experiments in different physical servers under CPU workload saturation. Obtained results show that the proposed index reflects the performance-energy trade-off behaviour and it helps systems’ administrators when consolidating virtual machines.

[1]  Andrei Tchernykh,et al.  Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores , 2019, Cluster Computing.

[2]  Carlos Juiz,et al.  On the Linearity of Performance and Energy at Virtual Machine Consolidation: The CiS2 Index for CPU Workload in Server Saturation , 2018, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[3]  Mark Horowitz,et al.  Energy dissipation in general purpose microprocessors , 1996, IEEE J. Solid State Circuits.

[4]  Samuel Kounev,et al.  Evaluating and Modeling Virtualization Performance Overhead for Cloud Environments , 2011, CLOSER.

[5]  Mark A. Holthouse,et al.  Experience with Automated Testing Analysis , 1979, Computer.

[6]  Nikita Jain,et al.  A Study on Green Cloud Computing , 2013 .

[7]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[8]  Ying Song,et al.  Analysis model for server consolidation of virtualized heterogeneous data centers providing internet services , 2018, Cluster Computing.

[9]  Sonja Filiposka,et al.  Improving the Energy Efficiency in Cloud Computing Data Centres Through Resource Allocation Techniques , 2017, Research Advances in Cloud Computing.

[10]  Kenneth M. Kempner Computers in Cardiology , 1975, Computer.

[11]  David A. Wood,et al.  Energy-Proportional Computing: A New Definition , 2017, Computer.

[12]  Stephen W. Poole,et al.  Revisiting Server Energy Proportionality , 2013, 2013 42nd International Conference on Parallel Processing.

[13]  Yonggang Wen,et al.  Data Center Energy Consumption Modeling: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[14]  Orlando Loques,et al.  Quantum virtual machine: power and performance management in virtualized web servers clusters , 2018, Clust. Comput..

[15]  Rajkumar Buyya,et al.  Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments , 2011, 2011 International Conference on Parallel Processing.

[16]  Marco Aiello,et al.  Metrics for Sustainable Data Centers , 2017, IEEE Transactions on Sustainable Computing.

[17]  David A. Wood,et al.  Pareto Governors for Energy-Optimal Computing , 2017, ACM Trans. Archit. Code Optim..

[18]  Samuel Kounev,et al.  Variations in CPU Power Consumption , 2016, ICPE.

[19]  Azizah Abdul Rahman,et al.  Server Consolidation: An Approach to make Data Centers Energy Efficient and Green , 2010, ArXiv.

[20]  L. Minas,et al.  Energy Efficiency for Information Technology: How to Reduce Power Consumption in Servers and Data Centers , 2009 .

[21]  Peter Kueng,et al.  Process performance measurement system: A tool to support process-based organizations , 2000 .

[22]  Lizhe Wang,et al.  Review of performance metrics for green data centers: a taxonomy study , 2011, The Journal of Supercomputing.

[23]  Carlos Juiz,et al.  Virtualization and consolidation: a systematic review of the past 10 years of research on energy and performance , 2018, The Journal of Supercomputing.

[24]  Xiaoqiang Ma,et al.  Enhancing Performance and Energy Efficiency for Hybrid Workloads in Virtualized Cloud Environment , 2018 .

[25]  Barbara Pernici,et al.  Managing the complex data center environment: an Integrated Energy-aware Framework , 2014, Computing.

[26]  A. Jain,et al.  Energy efficient computing- Green cloud computing , 2013, 2013 International Conference on Energy Efficient Technologies for Sustainability.

[27]  Inderveer Chana,et al.  Energy Efficiency Techniques in Cloud Computing , 2015, ACM Comput. Surv..

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

[29]  Kai Hwang,et al.  Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies , 2016, IEEE Transactions on Parallel and Distributed Systems.

[30]  Azizah Abdul Rahman,et al.  Energy efficiency and low carbon enabler green it framework for data centers considering green metrics , 2012 .

[31]  Florian Schmidt,et al.  On the Fly Orchestration of Unikernels: Tuning and Performance Evaluation of Virtual Infrastructure Managers , 2018, IEEE Transactions on Cloud Computing.

[32]  Kenneth J Arrow,et al.  THE POSSIBILITY OF A UNIVERSAL SOCIAL WELFARE FUNCTION , 1948 .

[33]  Jaume Salom,et al.  Improving Energy Efficiency in Data Centers and Federated Cloud Environments: Comparison of CoolEmAll and Eco2Clouds Approaches and Metrics , 2013, 2013 International Conference on Cloud and Green Computing.

[34]  Chi-Cheng Chuang,et al.  Evaluating Energy Efficiency of Data Centers with Generating Cost and Service Demand , 2012 .

[35]  Anand Sivasubramaniam,et al.  Worth their watts? - an empirical study of datacenter servers , 2010, HPCA - 16 2010 The Sixteenth International Symposium on High-Performance Computer Architecture.

[36]  Hermann de Meer,et al.  Performance tradeoffs of energy-aware virtual machine consolidation , 2013, Cluster Computing.

[37]  Kazuhiko Kato,et al.  Live Migration in Bare-metal Clouds , 2018 .

[38]  Weisong Shi,et al.  Energy efficiency comparison of hypervisors , 2019, Sustain. Comput. Informatics Syst..

[39]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[40]  Prasanta K. Jana,et al.  An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems , 2018, Cluster Computing.

[41]  Emiliano Casalicchio,et al.  A study on performance measures for auto-scaling CPU-intensive containerized applications , 2019, Cluster Computing.

[42]  Tapio Niemi,et al.  EEUI: a new measure to monitor and manage energy efficiency in data centers , 2018 .

[43]  Seddik Bacha,et al.  Efficiency metrics for qualification of datacenters in terms of useful workload , 2013, 2013 IEEE Grenoble Conference.