Modeling Power Consumption in Multicore CPUs with Multithreading and Frequency Scaling

The rapid growth of energy requirements in large data-center has motivated several research projects focusing on the reduction of power consumption. Several techniques have been studied to tackle this problem, and most of them require simple power models to estimate the energy consumption starting from known system parameters. It has been proven that the CPU is the component of a server that is most responsible for its total power consumption: for this reason several power models focusing on this resource has been developed. However, only a few accounts for standard CPU features like dynamic frequency scaling and hyperthreading, which can have a significant impact on the estimation accuracy. In this paper, we present the results from a set of experiments focusing on these CPU features, and we propose a simple power model able to provide accurate power estimates by taking them into account.

[1]  Ivan Stojmenovic,et al.  Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multicore Server Processors across Clouds and Data Centers , 2014, IEEE Transactions on Computers.

[2]  Lizy Kurian John,et al.  Predictive power management for multi-core processors , 2010, ISCA'10.

[3]  Irfan-Ullah Awan,et al.  Performance Evaluation of Local and Cloud Deployment of Web Clusters , 2011, 2011 14th International Conference on Network-Based Information Systems.

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

[5]  Xi He,et al.  Power-aware scheduling of virtual machines in DVFS-enabled clusters , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

[6]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[7]  Christos Kozyrakis,et al.  Full-System Power Analysis and Modeling for Server Environments , 2006 .

[8]  Krishna Kant.  A control scheme for batching DRAM requests to improve power efficiency , 2011, SIGMETRICS '11.

[9]  Xiao Zhang,et al.  Power containers: an OS facility for fine-grained power and energy management on multicore servers , 2013, ASPLOS '13.

[10]  Hong Zhu,et al.  A survey of practical algorithms for suffix tree construction in external memory , 2010 .

[11]  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.

[12]  Xiao Zhang,et al.  HaPPy: Hyperthread-aware Power Profiling Dynamically , 2014, USENIX Annual Technical Conference.

[13]  Isi Mitrani.  Trading Power Consumption against Performance by Reserving Blocks of Servers , 2012, EPEW/UKPEW.

[14]  Amer Diwan,et al.  The DaCapo benchmarks: java benchmarking development and analysis , 2006, OOPSLA '06.

[15]  George Goldberg,et al.  Leveraging disk drive acoustic modes for power management , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[16]  Kamel Barkaoui,et al.  A Versatile Traffic and Power Aware Performability Analysis of Server Virtualized Systems , 2014, 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems.

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

[18]  Juan Li,et al.  An overview of energy efficiency techniques in cluster computing systems , 2013, Cluster Computing.