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dc.contributor.authorPei, Pei
dc.contributor.authorHuo, Zongjie
dc.contributor.authorSanjuán Martínez, Óscar (1)
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.identifier.citationPei, 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.es_ES
dc.description.abstractPresently, 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, diffierences 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.es_ES
dc.relation.ispartofseries;vol. 12, nº 8
dc.subjectload balancinges_ES
dc.subjectgreen energyes_ES
dc.subjectgenetic algorithmes_ES
dc.subjectrenewable energyes_ES
dc.titleMinimal green energy consumption and workload management for data centers on smart city platformses_ES
dc.typeArticulo Revista Indexadaes_ES

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