Energy and locational workload management in data centers

Author(s):  
Sabrina Spatari ◽  
Nagarajan Kandasamy ◽  
Dara Kusic ◽  
Eugenia V. Ellis
2019 ◽  
Vol 55 (6) ◽  
pp. 5533-5542
Author(s):  
Fang Cao ◽  
Yajing Wang ◽  
Feng Zhu ◽  
Yujie Cao ◽  
Zhaohao Ding

2020 ◽  
Vol 12 (8) ◽  
pp. 3140 ◽  
Author(s):  
Pei Pei ◽  
Zongjie Huo ◽  
Oscar Sanjuán Martínez ◽  
Rubén González Crespo

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.


2014 ◽  
Vol 25 (8) ◽  
pp. 2030-2042 ◽  
Author(s):  
Yuanxiong Guo ◽  
Yanmin Gong ◽  
Yuguang Fang ◽  
Pramod P. Khargonekar ◽  
Xiaojun Geng

2012 ◽  
Vol 40 (1) ◽  
pp. 175-186 ◽  
Author(s):  
Zhenhua Liu ◽  
Yuan Chen ◽  
Cullen Bash ◽  
Adam Wierman ◽  
Daniel Gmach ◽  
...  

2017 ◽  
Vol 1 (1) ◽  
pp. 97-111 ◽  
Author(s):  
Agostino Forestiero ◽  
Carlo Mastroianni ◽  
Michela Meo ◽  
Giuseppe Papuzzo ◽  
Mehdi Sheikhalishahi

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