Accuracy-Enhanced Power Metering Technique in Virtualized Environments

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In virtualized environments, accurate metering the power consumption of individual virtual machine (VM) is a challenging issue. Conventional VM power metering techniques rely on the assumption that the power consumption is linear to the utilization of hardware utilization. However, such a utilization-based technique can only provide coarse-grained power measurement with unbounded error. In this paper, we firstly formulize the relationship between the resource utilization and the accuracy of power metering. Then, we proposed a novel VM scheduling algorithm, which uses the information of performance monitoring counters (PMC) to compensate the recursive power consumption. Theoretical analysis indicates that the proposed algorithm can provide bounded error when metering per-VM power consumption. Massive experiments are conducted by using various benchmarks on different platforms, and the results shown the error of per-VM power metering can be limited below 5.2%.
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Cloud Computing; Energy Efficiency; Resource Virtualization; Server Consolidation

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R.K. Jena, P.K. Mahanti, Computing in the Cloud: Concept and Trends,(2011) International Review on Computers and Software (IRECOS), 6 (1), pp. 1-10.

G. Dhiman, G. Marchetti, T. Rosing, vGreen: a System for Energy-efficient Management of Virtual Machines, ACM Trans. on Design Automation of Electronic Systems, Vol.16, No.1, pp.1-27, 2010.

R.K. Jena, Green Cloud Computing: Need of the Hour, (2012) International Review on Computers and Software (IRECOS), 7 (1), pp. 45-52.

X. Liao, H. Jin, H. Liu, Towards a Green Cluster through Dynamic Remapping of Virtual Machines, Future Generation Computer Systems, Vol.28, No.2, pp.469-477, 2012.

X. Liao, L. Hu, H. Jin, Energy Optimization Schemes in Cluster with Virtual Machines, Cluster Computing, Vol.13, No.2, pp.113-126, 2010.

P. Mahadevan, S. Banerjee, P. Sharma, et al., On Energy Efficiency for Enterprise and Data Center Networks, IEEE Communications Magazine, Vol.49, No.8, pp.94-100, 2011.

S. Kumar, V. Talwar, V. Kumar, et al., vManage: Loosely coupled Platform and Virtualization Management in Data Centers, Int’l Conf. on Autonomic Computing, pp.127-136, 2009.

L. Cherkasova, R. Gardner, Measuring CPU Overhead for I/O Processing in the XEN Virtual Machine Monitor, USENIX Annual Technical Conference, pp.387-390, 2005.

A. Kansal, F. Zhao, J. Liu, et al., Virtual Machine Power Metering and Provisioning, ACM Symp. on Cloud Computing, pp.39-50, 2010.

R. Koller, A. Verma, A. Neogi, WattApp: an Application Wware Power Meter for Shared Data Centers, Int’l Conf. on Autonomic Computing, pp.31-40, 2010.

A. Bohra, V. Chaudhary, VMeter: Power Modelling for Virtualized Clouds, Int’l Parallel and Distributed Processing Symposium, pp.1-8, 2010.

B. Krishnan, H. Amur, A. Gavrilovska, et al., VM Power Metering: Feasibility and Challenges, ACM SIGMETRICS Performance Evaluation Review, Vol.38, No.3, pp.56-60, 2010.

W.L. Bircher, L.K. John, Complete System Power Estimation using Processor Performance Events, IEEE Trans on Computers, Vol.61, No.4, pp.563-577, 2012.

J. Stoess, C. Lang, F. Bellosa, Energy Management for Hypervisor-based Virtual Machines, USENIX Annual Technical Conference, pp.1-14, 2007.

R. Bertran, Y. Becerra, D. Carrera, et al., Energy Accounting for Shared Virtualized Environments under DVFS using PMC-based Power Models, Future Generation Computer Systems, Vol.28, No.2, pp.457-468, 2012.

M.Y. Lim, A. Porterfield, R. Fowler, SoftPower: Fine-grain Power Estimations using Performance Counters, Int’l Symp. On High-Performance Distributed Computing, pp.308-311, 2010.

R. Bertran, M. Gonzàlez, X. Martorell, et al., Decomposable and Responsive Power Models for Multicore Processors using Performance Counters, ACM Int’l Conf. on Supercomputing, pp.147-158, 2010.

Oprofile., 2012.

SPEC CPU2006., 2012.

Transaction Processing Performance Council TPC-W., 2012.

Cachebench., 2012.

IOZone., 2012.

L. Cherkasova, D. Gupta, A. Vahdat, Comparison of the Three CPU Schedulers in XEN, ACM SIGMETRICS Performance Evaluation Review, Vol.35, No.2, pp.42-51, 2007.


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