Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms
1
School of Economics and Management, Lanzhou University of Technology, Lanzhou 730050, China
2
Computer Science Department, School of Engineering and Technology, Universidad Internacional de La Rioja, 26006 Logroño, La Rioja, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(8), 3140; https://doi.org/10.3390/su12083140
Received: 16 March 2020
/
Revised: 10 April 2020
/
Accepted: 10 April 2020
/
Published: 14 April 2020
(This article belongs to the Special Issue Green Energy and Smart Systems)
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.
View Full-Text
Keywords:
load balancing; green energy; genetic algorithm; renewable energy
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Pei, P.; Huo, Z.; Martínez, O.S.; Crespo, R.G. Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms. Sustainability 2020, 12, 3140. https://doi.org/10.3390/su12083140
AMA Style
Pei P, Huo Z, Martínez OS, Crespo RG. Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms. Sustainability. 2020; 12(8):3140. https://doi.org/10.3390/su12083140
Chicago/Turabian StylePei, Pei, Zongjie Huo, Oscar S. Martínez, and Rubén G. Crespo. 2020. "Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms" Sustainability 12, no. 8: 3140. https://doi.org/10.3390/su12083140
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
