Workload management for power efficiency in virtualized data centers

2011 ◽  
Vol 54 (7) ◽  
pp. 131-141 ◽  
Author(s):  
Gargi Dasgupta ◽  
Amit Sharma ◽  
Akshat Verma ◽  
Anindya Neogi ◽  
Ravi Kothari
Keyword(s):  
Power Efficiency ◽  
Data Centers ◽  
Workload Management ◽  
Virtualized Data Centers

Preemption-Aware Energy Management in Virtualized Data Centers

2012 ◽  
Author(s):  
Mohsen Amini Salehi ◽  
P. Radha Krishna ◽  
Krishnamurty Sai Deepak ◽  
Rajkumar Buyya
Keyword(s):  
Energy Management ◽  
Data Centers ◽  
Virtualized Data Centers

UPS Node-Based Workload Management for Data Centers Considering Flexible Service Requirements

2019 ◽  
Vol 55 (6) ◽  
pp. 5533-5542
Author(s):  
Fang Cao ◽  
Yajing Wang ◽  
Feng Zhu ◽  
Yujie Cao ◽  
Zhaohao Ding
Keyword(s):  
Data Centers ◽  
Workload Management

scholarly journals Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms

2020 ◽  
Vol 12 (8) ◽  
pp. 3140 ◽  
Author(s):  
Pei Pei ◽  
Zongjie Huo ◽  
Oscar Sanjuán Martínez ◽  
Rubén González Crespo
Keyword(s):  
Energy Consumption ◽  
Smart City ◽  
Energy Use ◽  
Data Centers ◽  
Renewable Energy Sources ◽  
Global Market ◽  
Green Energy ◽  
Workload Management ◽  
Multi Objective Genetic Algorithm ◽  
Delay Tolerant

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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