Using Frequency Scaling on Virtualized Memory in Cloud Datacenters

As the increasing of IT-infrastructure in cloud platforms, rapidly growth of energy consumption becomes a critical problem in many cloud datacenters. Conventionally, most of studies on energy-efficiency optimization concentrate on CPU related energy costs instead of memory subsystem, since CPU often dominates the total energy consumption in modern servers. However, such a situation is gradually changing as more and more cloud datacenters are equipped with larger and larger memory systems for dealing with dataintensive applications. In this paper, we present a novel mechanism, namely frequency scaling on virtualized memory (FSVM), which applies DVFS technology on memory subsystem based on the characteristics of active VM instances. Comparing with previous studies, our approach provides a fine-grained memory energy consumption conservation mechanism for virtualized servers. Extensive experiments are conducted to investigate the effectiveness and performance of our FSVM, and the results indicate that it can significantly improve the energy-efficiency of memory subsystem in virtualized servers.

[1]  Erich Schikuta,et al.  Toward an economic and energy‐aware cloud cost model , 2013, Concurr. Comput. Pract. Exp..

[2]  Manuel E. Acacio,et al.  On the design of energy‐efficient hardware transactional memory systems , 2013, Concurr. Comput. Pract. Exp..

[3]  Manuel E. Acacio,et al.  Selective dynamic serialization for reducing energy consumption in hardware transactional memory systems , 2013, The Journal of Supercomputing.

[4]  Hakim Weatherspoon,et al.  Plug into the Supercloud , 2013, IEEE Internet Computing.

[5]  Zhao Zhang,et al.  Mini-Rank: A Power-EfficientDDRx DRAM Memory Architecture , 2014, IEEE Transactions on Computers.

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

[7]  Jordi Guitart,et al.  A service framework for energy-aware monitoring and VM management in Clouds , 2013, Future Gener. Comput. Syst..

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

[9]  Wei Chen,et al.  A three-phase energy-saving strategy for cloud storage systems , 2014, J. Syst. Softw..

[10]  Ami Marowka.  Maximizing energy saving of dual-architecture processors using DVFS , 2014, The Journal of Supercomputing.

[11]  Zhigang Hu,et al.  An Energy-Aware Heuristic Scheduling for Data-Intensive Workflows in Virtualized Datacenters , 2013, Journal of Computer Science and Technology.

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

[13]  Jens Myrup Pedersen,et al.  Using latency as a QoS indicator for global cloud computing services , 2013, Concurr. Comput. Pract. Exp..

[14]  Avinash Karanth Kodi,et al.  Extending the Performance and Energy-Efficiency of Shared Memory Multicores with Nanophotonic Technology , 2014, IEEE Transactions on Parallel and Distributed Systems.

[15]  Christoforos E. Kozyrakis,et al.  Improving System Energy Efficiency with Memory Rank Subsetting , 2012, TACO.

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

[17]  Xiao Qin,et al.  PRE-BUD: Prefetching for energy-efficient parallel I/O systems with buffer disks , 2011, TOS.

[18]  Kirk W. Cameron,et al.  Memory MISER: Improving Main Memory Energy Efficiency in Servers , 2009, IEEE Transactions on Computers.

[19]  Zhao Zhang,et al.  Decoupled DIMM: building high-bandwidth memory system using low-speed DRAM devices , 2009, ISCA '09.

[20]  Zibin Zheng,et al.  QoS Ranking Prediction for Cloud Services , 2013, IEEE Transactions on Parallel and Distributed Systems.

[21]  Kenli Li,et al.  An Adaptive Energy-Conserving Strategy for Parallel Disk Systems , 2008, 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications.

[22]  John D. Davis,et al.  Including Variability in Large-Scale Cluster Power Models , 2012, IEEE Computer Architecture Letters.

[23]  Yue-Shan Chang,et al.  Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments , 2013, The Journal of Supercomputing.