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

Presently, energy is considered a significant resource that grows scarce with high demand and population in the global market. Therefore, a survey suggested that renewable energy sources are required to avoid scarcity. Hence, in this paper, a smart, sustainable probability distribution hybridized genetic approach (SSPD-HG) has been proposed to decrease energy consumption and minimize the total completion time for a single machine in smart city machine interface platforms. Further, the estimated set of non-dominated alternative using a multi-objective genetic algorithm has been hybridized to address the problem, which is mathematically computed in this research. This paper discusses the need to promote the integration of green energy to reduce energy use costs by balancing regional loads. Further, the timely production of delay-tolerant working loads and the management of thermal storage at data centers has been analyzed in this research. In addition, differences in bandwidth rates between users and data centers are taken into account and analyzed at a lab scale using SSPD-HG for energy-saving costs and managing a balanced workload.

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Power reduction effect of higher room temperature operation in data centers

2010 IEEE Network Operations and Management Symposium - NOMS 2010 ◽

10.1109/noms.2010.5488420 ◽

2010 ◽

Cited By ~ 3

Author(s):

Kenichi Muroya ◽

Takeshi Kinoshita ◽

Hiroyuki Tanaka ◽

Minori Youro

Keyword(s):

Room Temperature ◽

Data Centers ◽

Power Reduction ◽

Reduction Effect ◽

Room Temperature Operation

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Power Reduction Techniques in Cloud Data Centers

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1061-1062.1070 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.1148 ◽

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.

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Dynamic voltage/frequency scaling for power reduction in data centers: Enough or not?

2009 ISECS International Colloquium on Computing, Communication, Control, and Management ◽

10.1109/cccm.2009.5267619 ◽

2009 ◽

Cited By ~ 4

Author(s):

Congfeng Jiang ◽

Jian Wan ◽

Xianghua Xu ◽

Yunfa Li ◽

Xindong You ◽

...

Keyword(s):

Data Centers ◽

Power Reduction ◽

Frequency Scaling ◽

Dynamic Voltage ◽

Voltage Frequency

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

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2019010105 ◽

2019 ◽

Vol 9 (1) ◽

pp. 59-81 ◽

Cited By ~ 7

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.

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