Virtual Machine Consolidation for Datacenter Energy Improvement

Rapid growth and proliferation of cloud computing services around the world has increased the necessity and significance of improving the energy efficiency of could implementations. Virtual machines (VM) comprise the backend of most, if not all, cloud computing services. Several VMs are often consolidated on a physical machine to better utilize its resources. We take into account the cooling and network structure of the datacenter hosting the physical machines when consolidating the VMs so that fewer racks and routers are employed, without compromising the service-level agreements, so that unused routing and cooling equipment can be turned off to reduce energy consumption. Our experimental results on four benchmarks shows that our technique improves energy consumption of servers, network equipment, and cooling systems by 2.5%, 18.8%, and 28.2% respectively, resulting in a total of 14.7% improvement on average in the entire datacenter.

[1]  Wang Xiaoli,et al.  An Energy-Aware VMs Placement Algorithm in Cloud Computing Environment , 2012, 2012 Second International Conference on Intelligent System Design and Engineering Application.

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

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

[4]  Massoud Pedram,et al.  Temperature-aware dynamic resource provisioning in a power-optimized datacenter , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[5]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

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

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

[8]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[9]  Jun Yan,et al.  A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing , 2010, 2010 Ninth International Conference on Grid and Cloud Computing.

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

[11]  Chuang Lin,et al.  Energy optimized modeling for live migration in virtual data center , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[12]  Ching-Chi Lin,et al.  Energy-Aware Virtual Machine Dynamic Provision and Scheduling for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[13]  Mor Harchol-Balter,et al.  Optimal power allocation in server farms , 2009, SIGMETRICS '09.