An Energy-Efficient VM Placement in Cloud Datacenter

Energy efficiency of cloud computing attracts a great deal of attention recently more than ever. In this paper, based on the characteristics of virtual cloud environment and MapReduce workload, an energy-efficient VM placement is presented. The method involves two algorithms: Tight Recipe Packing (TRP) and Virtual Cluster Scaling (VCS). TRP aims at minimizing an energy consumption and making trade-off between VM duration and resource utilization, so that data center can place the VMs in request to the least amount of physical servers. VCS further enhances capacity utilization of active physical servers while reducing the energy consumption, which can work together as a complement with other existing placement algorithms. In addition, an estimation method is proposed to predict the completion time of a running MapReduce job. The experimental results both in simulation and Hadoop test bed show that our approach achieves greater energy savings over existing algorithms.

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

[2]  Christoforos E. Kozyrakis,et al.  On the energy (in)efficiency of Hadoop clusters , 2010, OPSR.

[3]  Nam Thoai,et al.  EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud , 2013, Trans. Large Scale Data Knowl. Centered Syst..

[4]  Rong Ge,et al.  Improving MapReduce energy efficiency for computation intensive workloads , 2011, 2011 International Green Computing Conference and Workshops.

[5]  Xiaohua Jia,et al.  Energy Saving Virtual Machine Allocation in Cloud Computing , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[6]  Abhishek Chandra,et al.  Exploiting Spatio-Temporal Tradeoffs for Energy-Aware MapReduce in the Cloud , 2012, IEEE Transactions on Computers.

[7]  Archana Ganapathi,et al.  To compress or not to compress - compute vs. IO tradeoffs for mapreduce energy efficiency , 2010, Green Networking '10.

[8]  Zhao Li,et al.  Scheduling real-time workflow on MapReduce-based cloud , 2013, Third International Conference on Innovative Computing Technology (INTECH 2013).

[9]  Albert G. Greenberg,et al.  Reining in the Outliers in Map-Reduce Clusters using Mantri , 2010, OSDI.

[10]  Maolin Tang,et al.  A simulated annealing algorithm for energy efficient virtual machine placement , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[11]  Jean-Marc Menaud,et al.  Performance and Power Management for Cloud Infrastructures , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[12]  Fei Teng,et al.  Cloud Computing: Data-Intensive Computing and Scheduling , 2012 .

[13]  Jordi Torres,et al.  Towards energy-aware scheduling in data centers using machine learning , 2010, e-Energy.

[14]  Alan J. Rowe,et al.  Beyond expert systems—Reasoning, judgment, and wisdom , 1992 .

[15]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[16]  Abhishek Chandra,et al.  Exploiting Spatio-temporal Tradeoffs for Energy-Aware MapReduce in the Cloud , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[17]  Stefano Nolfi,et al.  Good teaching inputs do not correspond to desired responses in ecological neural networks , 1994, Neural Processing Letters.

[18]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[19]  Xia Li,et al.  Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers , 2014, Expert Syst. Appl..

[20]  Maolin Tang,et al.  A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers , 2014, Neural Processing Letters.

[21]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

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

[23]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[24]  Jignesh M. Patel,et al.  Energy management for MapReduce clusters , 2010, Proc. VLDB Endow..

[25]  Jordi Torres,et al.  Energy-Aware Scheduling in Virtualized Datacenters , 2010, 2010 IEEE International Conference on Cluster Computing.