Energy-Efficient Data Center Networks Planning with Virtual Machine Placement and Traffic Configuration

2014 ◽  
Author(s):  
Ting Yang ◽  
Young Choon Lee ◽  
Albert Y. Zomaya
Keyword(s):  
Virtual Machine ◽  
Data Center ◽  
Energy Efficient ◽  
Virtual Machine Placement ◽  
Data Center Networks ◽  
Efficient Data

AI-Assisted Knowledge-Defined Network Orchestration for Energy-Efficient Data Center Networks

2020 ◽  
Vol 58 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Wei Lu ◽  
Lipei Liang ◽  
Bingxin Kong ◽  
Baojia Li ◽  
Zuqing Zhu
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Data Center Networks ◽  
Efficient Data ◽  
Network Orchestration

scholarly journals Balanced and Energy Efficient Multi Core-Aware Virtual Machine Placement Algorithm Using Multi-Dimensional Resource Space Partition Model

2021 ◽  
Author(s):  
Nagadevi ◽  
Kasmir Raja
Keyword(s):  
Resource Allocation ◽  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Energy Efficient ◽  
Virtual Machine Placement ◽  
Partition Model ◽  
Space Partition ◽  
Resource Space ◽  
Placement Algorithm

Optimal resource management is required in a data center to allocate the resources to users in a balanced manner. Balanced resource allocation is one of the key challenges in the data center. The multi-dimensional resources of a data center must be allocated in a balanced manner in all the dimensions of physical machines. The unbalanced resource allocation leads to unused residual resource fragments. The unused residual resource fragments leads to resource wastage. If the multi-dimensional data center resources are allocated in a balanced manner, the resource wastage does not occur. Also, the balanced allocation improves the power consumption. The balanced resource allocation reduces the resource wastage as well as reduces the power consumption. In this paper, we have designed a Balanced Energy Efficient Multi-Core Aware Virtual Machine Placement algorithm (MCA-BEE-VMP) using multi-dimensional resource space partition model to balance the resources like CPU and memory and also to reduce the power consumption. We used Google Cloud Jobs (GoCJ) dataset for the simulation. In our simulation of MCA-BEE-VMP using Cloud Sim simulation tool we have achieved balanced CPU and memory resources allocation in two dimensions of a physical machine. The resource wastage and power consumption is improved and the simulation results were analyzed.

Sign in / Sign up

Export Citation Format

Share Document