Measurement of energy efficiency metrics of data centers. case study: higher education institution of Barranquilla

Data centers have become fundamental pillars of the network infrastructures of the various companies or entities regardless of their size. Since they support the processing, analysis, assurance of the data generated in the network, and by the applications in the cloud, which every day increases its volume thanks to diverse and sophisticated technologies. The management and storage of this large volume of information make the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Center (GDC) is a solution for this problem, reducing the impact produced by the data centers in the environment through the monitoring and control of these and to the application of standards-based on metrics. Although each data center has its particularities and requirements, the metrics are the tools that allow us to measure the energy efficiency of the data center and evaluate if it is friendly to the environment (1.Adv. Intell. Syst. Comput. 574:329–340). The objective of the study is to calculate these metrics in the data centers of a Higher Education Institution in Barranquilla, on both campuses, and the analysis of these will be carried out. It is planned to extend this study by reviewing several metrics to conclude, which is the most efficient and which allows defining the guidelines to update or convert the data center in a friendly environment. The research methodology used for the development of the project is descriptive and no-experimental

Water quality management could halve future water scarcity cost-effectively in the Pearl River Basin

Reducing water scarcity requires both mitigation of the increasing water pollution and adaptation to the changing availability and demand of water resources under global change. However, state-of-the-art water scarcity modeling efforts often ignore water quality and associated biogeochemical processes in the design of water scarcity reduction measures. Here, we identify cost-effective options for reducing future water scarcity by accounting for water quantity and quality in the highly water stressed and polluted Pearl River Basin in China under various socio-economic and climatic change scenarios based on the Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Our modeling approach integrates a nutrient model (MARINA-Nutrients) with a cost-optimization procedure, considering biogeochemistry and human activities on land in a spatially explicit way. Results indicate that future water scarcity is expected to increase by a factor of four in most parts of the Pearl River Basin by 2050 under the RCP8.5-SSP5 scenario. Results also show that water quality management options could half future water scarcity in a cost-effective way. Our analysis could serve as an example of water scarcity assessment for other highly water stressed and polluted river basins around the world and inform the design of cost-effective measures to reduce water scarcity

Microcellular Electrode Material for Microbial Bioelectrochemical Systems Synthesized by Hydrothermal Carbonization of Biomass Derived Precursors

V.F. acknowledges a UQ Postdoctoral Fellowship. This work was supported by the Australian Research Council Grant DP110100539. The authors acknowledge the facilities and the scientific and technical assistance of the Australian Microscopy & Microanalysis Research Facility at the Centre for Microscopy and Microanalysis (The University of Queensland). The Ghent University Special Research Fund (BOF) is acknowledged for the postdoctoral grant of M.N.B

An Exhaustive Study of Possibility Measures of Interval-Valued Intuitionistic Fuzzy Sets and Application to Multicriteria Decision Making

This work is interested in showing the importance of possibility theory in multicriteria decision making (MCDM). Thus, we apply some possibility measures from literature to the MCDM method using interval-valued intuitionistic fuzzy sets (IVIFSs). These measures are applied to a decision matrix after being transformed with aggregation operators. The results are compared between each other and concluding remarks are drawn

CE-D2D: Collaborative and Popularity-aware Proactive Chunks Caching in Edge Networks

Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging paradigm, where cloud computing services are extended to edge networks to allocate multimedia contents close to end-users. However, despite minimizing the traffic over the content delivery networks (CDN), congestions may occur in peak hours characterized by high load demands. Involving users' devices in data offloading through Device-to-Device (D2D) connections has proved its efficiency in relieving the cellular spectrum utilization. In this paper, the Collaborative Edge network (CE) and the devices (D2D) cluster are combined to form a CE-D2D framework aiming at maximizing video caching and efficiently using cellular and backhaul bandwidths. However, since we are dealing with large sized contents, the small storage and bandwidth capacities offered by users limit the number of cached videos and restrict offloading large volume data. This makes the CE-D2D framework, so far, an incomplete solution for multimedia contents. Therefore, we propose a caching strategy to cache only the chunks of videos to be watched and instead of caching or offloading each video content by one edge node (as performed in literature), helpers (MEC and mobiles) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. In this work, we model both CE and D2D frameworks as linear programs and schedule the collaboration between them constrained by resource availability. Due to the NP-hardness of the problem, we introduce an online heuristic that presents a proactive chunks caching (HLPC) and a near-optimal data offloading with polynomial complexity. 2020 IEEE.Qatar National Research FundScopu

Collaborative hierarchical caching and transcoding in edge network with CE-D2D communication

To support multimedia applications, Mobile Edge Computing (MEC) servers offer storage and computing capacities to handle videos close to end-users. However, the high load in peak hours consumes the limited available bandwidth of existing cellular and backhaul links leading to low network performance. Hence, an elastic system model is required to maintain the high Quality of Experience (QoE) as the resource demands increase. Caching popular videos at mobile devices is considered a promising technique for content delivery. Yet, mobile users offer small capacities that are not adequate for large-sized video sharing. In this paper, we extend the collaborative caching and processing framework in edge networks (Collaborative Edge - CE) to include the users' mobile video sharing (Device-to-Device - D2D). We propose a caching strategy to cache only the chunks of videos to be watched and instead of offloading one video content by one edge node, helpers (MEC servers and users) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. To only cache popular contents, we designed a D2D-aware proactive chunks caching on users devices based on our chunks popularity model. Next, we formulate this CE-D2D collaborative problem as a linear program. Due to the NP-hardness of the problem, we introduce a sub-optimal relaxation and an online heuristic using the proactive caching and presenting a near optimal data offloading and a profitable payment determination, with polynomial time complexity. The simulation results show that our policies and heuristics outperform other edge caching approaches by more than 10% in terms of hit ratio, average delay, and cost. 2020 Elsevier LtdQatar Foundation;Qatar National Research FundScopu