Energy-Efficient VM-Placement in Cloud Data Center
Abstract
Different mapping relationships between virtual machines (VMs) and physical machines (PMs) in cloud data centers cause different resource utilization, so how to place Energy-Efficient VM-Placement in Cloud Data Center on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers.
Energy-Efficient VM-Placement in Cloud Data Center To address the issue, this paper proposes a VM placement scheme that meets multiple resource constraints, such as physical server size (CPU, memory, storage, bandwidth, etc.) and network connectivity capacity to improve resource utilization and reduce both the number of active physical servers and network elements to ultimately reduce energy consumption.
System Configuration
H/W System Configuration
Speed : 1.1 GHz
RAM : 256 MB(min)
Hard Disk : 20 GB
Floppy Drive : 1.44 MB
Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : SVGA
S/W System Configuration
Platform : cloud computing
Operating system : Windows Xp,7,
Server : WAMP/Apache
Working on : Browser Like Firefox, IE
Conclusion
This work is presented in IaaS cloud, especially for MapReduce workloads. The proposed TRP outputs the original RP and BD (T=100) by saving on Hadoop testbed 16 percent and 13 percent energy consumption. Meanwhile, the parameter adjustment in BD can be avoided by repeated empirical tests. Several homogeneous cases test TRP and it is easy to extend to other heterogeneous scenarios.
