Power reduction in HPC data centers: a joint server placement and chassis consolidation approach

2014 ◽  
Vol 70 (2) ◽  
pp. 845-879 ◽  
Author(s):  
Ali Pahlavan ◽  
Mahmoud Momtazpour ◽  
Maziar Goudarzi
Keyword(s):  
Data Centers ◽  
Power Reduction ◽  
Server Placement

Power Reduction Techniques in Cloud Data Centers

2014 ◽  
Vol 1061-1062 ◽  
pp. 1070-1073
Author(s):  
Lei Tang ◽  
Zheng Ce Cai ◽  
Guo Long Chen ◽  
Xian Wei Li
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Large Fraction ◽  
Power Reduction ◽  
Cloud Services ◽  
Total Power ◽  
Cloud Data ◽  
Reduction Techniques ◽  
Total Power Consumption ◽  
Cloud Data Centers

In recent years, cloud computing has received much attention from both academia and engineering areas. With more and more companies beginning to provide cloud services, more and more data centers are being built. Recent studies show that the energy consumed by cloud data centers accounts for a large fraction of the total power consumption today. This motivates us to survey power reduction techniques in cloud data centers.

scholarly journals Towards Dynamic Voltage/Frequency Scaling for Power Reduction in Data Centers

2010 ◽  
Vol 20-23 ◽  
pp. 1148-1156
Author(s):  
Cong Feng Jiang ◽  
Ying Hui Zhao ◽  
Jian Wan
Keyword(s):  
Data Centers ◽  
Heat Dissipation ◽  
Power Reduction ◽  
Current Data ◽  
Frequency Scaling ◽  
Reduction Techniques ◽  
Higher Power ◽  
Dynamic Voltage ◽  
Management Schemes ◽  
Voltage Frequency

Higher power consumption in data centers results in more heat dissipation, cooling costs and degrades the system reliability. Conventional power reduction techniques such as dynamic voltage/frequency scaling (DVS/DFS) have disadvantages when they are ported to current data centers with virtualization deployments. In this paper, we give a short survey and discussion on some issues and aspects of DVS/DFS in data centers. This paper also presents a simple comparison of four power management schemes in virtualization environments.

Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

2019 ◽  
Vol 9 (1) ◽  
pp. 59-81 ◽  
Author(s):  
Jenia Afrin Jeba ◽  
Shanto Roy ◽  
Mahbub Or Rashid ◽  
Syeda Tanjila Atik ◽  
Md Whaiduzzaman
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Dynamic Scheduling ◽  
Random Search ◽  
Power Reduction ◽  
Vm Migration ◽  
Green Cloud Computing ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

Data centers power reduction: A two time scale approach for delay tolerant workloads

2012 ◽  
Author(s):  
Yuan Yao ◽  
Longbo Huang ◽  
Abhihshek Sharma ◽  
Leana Golubchik ◽  
Michael Neely
Keyword(s):  
Time Scale ◽  
Data Centers ◽  
Power Reduction ◽  
Delay Tolerant

Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

2021 ◽  
pp. 846-872
Author(s):  
Jenia Afrin Jeba ◽  
Shanto Roy ◽  
Mahbub Or Rashid ◽  
Syeda Tanjila Atik ◽  
Md Whaiduzzaman
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Dynamic Scheduling ◽  
Random Search ◽  
Power Reduction ◽  
Vm Migration ◽  
Green Cloud Computing ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

Queuing Model Based Data Centers: A Review

2019 ◽  
Vol 123 ◽  
pp. 11-20 ◽  
Author(s):  
N. Thirupathi Rao ◽  
Debnath Bhattacharyya ◽  
S. Naga Mallik Raj
Keyword(s):  
Data Centers ◽  
Queuing Model ◽  
Model Based
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