Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets

2014 ◽  
Vol 25 (10) ◽  
pp. 2659-2669 ◽  
Author(s):  
Huajie Shao ◽  
Lei Rao ◽  
Zhi Wang ◽  
Xue Liu ◽  
Zhibo Wang ◽  
...  
Keyword(s):  
Load Balancing ◽  
Electricity Markets ◽  
Energy Cost ◽  
Data Centers ◽  
Cost Management ◽  
Optimal Load

Coordinated Energy Cost Management of Distributed Internet Data Centers in Smart Grid

2012 ◽  
Vol 3 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Lei Rao ◽  
Xue Liu ◽  
Le Xie ◽  
Wenyu Liu
Keyword(s):  
Smart Grid ◽  
Energy Cost ◽  
Data Centers ◽  
Cost Management

scholarly journals Optimal Load-Balancing for Heterogeneous Clusters

2015 ◽  
Vol 43 (3) ◽  
pp. 43-43 ◽  
Author(s):  
Anshul Gandhi ◽  
Naman Mittal ◽  
Xi Zhang
Keyword(s):  
Load Balancing ◽  
Heterogeneous Clusters ◽  
Optimal Load

Design of load balancing mechanism for Indian electricity markets

2016 ◽  
Vol 8 (2) ◽  
pp. 309-350 ◽  
Author(s):  
Hameed Safiullah ◽  
Gabriela Hug ◽  
Rahul Tongia
Keyword(s):  
Load Balancing ◽  
Electricity Markets

Energy-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

2021 ◽  
Vol 11 (3) ◽  
pp. 34-48
Author(s):  
J. K. Jeevitha ◽  
Athisha G.
Keyword(s):  
Quality Of Service ◽  
Load Balancing ◽  
Energy Efficient ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data ◽  
The Third ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

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