A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine (VM) deployment algorithm called three-threshold energy saving algorithm (TESA), which is based on the linear relation between the energy consumption and (processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is, host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies (minimization of migrations policy based on TESA (MIMT), maximization of migrations policy based on TESA (MAMT), highest potential growth policy based on TESA (HPGT), lowest potential growth policy based on TESA (LPGT) and random choice policy based on TESA (RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold (ST) algorithm and minimization of migrations (MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

[1]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[2]  Parthasarathy Ranganathan.  Recipe for efficiency: principles of power-aware computing , 2010, CACM.

[3]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

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

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

[6]  Hui Wang,et al.  Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

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

[8]  Sanjay Ranka,et al.  Dynamic slack allocation algorithms for energy minimization on parallel machines , 2010, J. Parallel Distributed Comput..

[9]  Albert Y. Zomaya,et al.  Energy efficient utilization of resources in cloud computing systems , 2010, The Journal of Supercomputing.

[10]  Erik Elmroth,et al.  Unifying Cloud Management: Towards Overall Governance of Business Level Objectives , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[11]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[12]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

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

[14]  Karthick Rajamani,et al.  Thermal response to DVFS: analysis with an Intel Pentium M , 2007, Proceedings of the 2007 international symposium on Low power electronics and design (ISLPED '07).

[15]  Michael Kistler,et al.  The case for power management in web servers , 2002 .

[16]  Rajkumar Buyya,et al.  Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation , 2009, CloudCom.