The Research of Measuring Approach and Energy Efficiency for Hadoop Periodic Jobs

2015 ◽  
Vol 8 (1) ◽  
pp. 206-210 ◽  
Author(s):  
Yu Junyang ◽  
Hu Zhigang ◽  
Han Yuanyuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Estimate ◽  
Cloud Platform ◽  
Periodic Tasks ◽  
Current Consumption ◽  
Reduce Energy Consumption

Current consumption of cloud computing has attracted more and more attention of scholars. The research on Hadoop as a cloud platform and its energy consumption has also received considerable attention from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on the platform of Hadoop. Combining with the current mainstream of energy estimate formula to conduct further analysis, this paper has reached a conclusion as how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods as being energy-saving strategies and verify the feasibility of the methods for the measurement of periodic tasks at the same time.

Energy-Efficient Technologies in the Construction of School in Hot Climates

2014 ◽  
Vol 587-589 ◽  
pp. 287-293 ◽  
Author(s):  
Anastasiia Sergeevna Fidrikova ◽  
Olga Sergeevna Grishina ◽  
Alexey Pavlovich Marichev ◽  
Xeniya Mikhailovna Rakova
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Efficient ◽  
Climatic Conditions ◽  
Energy Reduction ◽  
Insulating Materials ◽  
Space Planning ◽  
Class A ◽  
Reduce Energy Consumption

Reduction of the costs in the operation of the building, due to energy-saving technologies, is a priority in the construction today. This article discusses some ways to reduce energy consumption of schools in hot climates such as the installation of solar collectors, using of triple-glazed windows and modern insulating materials. These methods of energy reduction are determined by the selected space-planning solutions, constructive features of the structure, financial possibilities and climatic conditions. Considering these above listed characteristics, the school was designed for the class A of energy efficiency.[1-4]

Research on High-Rise Buildings Energy Efficiency Design in Advanced Structure

2013 ◽  
Vol 700 ◽  
pp. 89-92
Author(s):  
Juan Li ◽  
Ying Pan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Building Materials ◽  
International Standards ◽  
Building Industry ◽  
General Interest ◽  
High Rise ◽  
The Core ◽  
Reduce Energy Consumption

Early in the 20th century 70's, the concept of energy-saving building was officially proposed. The core of energy-saving buildings is to reduce energy consumption and enhance energy efficiency in buildings. However, with the continued rapid growth of China's economic and urbanization high-rise buildings have become the mainstream of the building industry. So, the research on energy-saving design in high-rise buildings in advanced structure becomes the hot issue of general interest. Many advanced structures and building materials have constantly developed, and have been used in high-rise buildings energy efficiency design. This paper summarizes and prospects the current situation of energy-saving design in chinas high-rise building, and also provides a reference for hoping the energy-saving design can be geared to international standards better.

Research on Energy-Saving Algorithms with Migration Energy Consumption in Heterogeneous Cloud Environment

2014 ◽  
Vol 986-987 ◽  
pp. 1383-1386
Author(s):  
Zhen Xing Yang ◽  
He Guo ◽  
Yu Long Yu ◽  
Yu Xin Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Large Scale ◽  
Electrical Energy ◽  
Cloud Environment ◽  
Migration Energy ◽  
Live Migration ◽  
Large Scale Data ◽  
Heterogeneous Cloud

Cloud computing is a new emerging paradigm which delivers an infrastructure, platform and software as services in a pay-as-you-go model. However, with the development of cloud computing, the large-scale data centers consume huge amounts of electrical energy resulting in high operational costs and environment problem. Nevertheless, existing energy-saving algorithms based on live migration don’t consider the migration energy consumption, and most of which are designed for homogeneous cloud environment. In this paper, we take the first step to model energy consumption in heterogeneous cloud environment with migration energy consumption. Based on this energy model, we design energy-saving Best fit decreasing (ESBFD) algorithm and energy-saving first fit decreasing (ESFFD) algorithm. We further provide results of several experiments using traces from PlanetLab in CloudSim. The experiments show that the proposed algorithms can effectively reduce the energy consumption of data center in the heterogeneous cloud environment compared to existing algorithms like NEA, DVFS, ST (Single Threshold) and DT (Double Threshold).

ФОРМУВАННЯ МЕРЕЖІ ЦЕНТРІВ ЕНЕРГОХАБА ЗНАНЬ ДЛЯ ПРОГНОЗУВАННЯ ПРІОРИТЕТНОГО НАПРЯМУ РОЗВИТКУ ПРОГРАМ ЕНЕРГОЕФЕКТИВНОСТІ ТА ЕНЕРГОЗБЕРЕЖЕННЯ В ЗАКЛАДАХ ВИЩОЇ ОСВІТИ

2022 ◽  
pp. 35-44
Author(s):  
Ivan M. Gryshchenko ◽  
Mykhailo O. Verhun ◽  
Andrii S. Prokhorovskyi
Keyword(s):  
Higher Education ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Higher Education Institutions ◽  
Action Plan ◽  
Energy Sources ◽  
Energy Supply System ◽  
Depth Analysis ◽  
The University

This article attempts to verify the relevance of building a network of energy knowledge hub centres to tackle the priority objective in enhancing energy efficiency and energy saving management in higher education institutions. It is emphasized that the issues of careful and wise use of fuels and energy resources challenge more government efforts, active use of advanced projects to manage energy saving and energy efficiency through the integrated use of different energy sources. The study argues that to identify the potential for energy saving, setting regulatory indicators of energy consumption, determining the key energy saving measures and target objects in the public sector where energy saving programs are planned to be implemented, there is a need to conduct energy surveys with further developing of energy passports for buildings. In the frameworks of this study, the following research methods were used: abstract and logical analysis – to interpret the essence of energy saving concepts for universities; systemic approach – to identify the specifics of energy saving projects implementation in universities; in-depth analysis and synthesis – to forecast the university development priority area of the "Energy efficiency and energy saving"; system, structural, comparative and statistical analyses – to assess the energy consumption in universities; economic and statistical methods – to evaluate the level and the dynamics of the energy sources use before and after the implementation of project activities; graph-based and analytical methods – to facilitate visual representation and schematic presentation of forecasts for further development of energy efficiency and energy saving systems. The study offers a mechanism to shape a network of energy knowledge hub centres to forecast a priority development area of energy efficiency and energy saving programs in higher education institutions along with providing an overview on the process of energy saving based on energy knowledge hub centres by carrying out the following tasks: project identification, scanning, energy audit, implementation of an action plan, and monitoring. It has been verified that to enhance the energy supply system in the university buildings, the following objectives should be attained: using the energy knowledge hub to forecast the university energy efficiency and energy saving programme, implementing an automated individual heating station with weather regulation and installing new radiator heaters.

Application of BIM Technology on Energy Efficiency Building Design

2014 ◽  
Vol 587-589 ◽  
pp. 283-286 ◽  
Author(s):  
Mei Zhang
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Design Method ◽  
Building Design ◽  
Building Energy ◽  
Information Modeling ◽  
Building Energy Consumption ◽  
Analysis Software ◽  
Building Model

According to the current application situation and domestic energy of our current building energy efficiency design analysis software, in view of the current traditional energy-saving design method can't meet the need of practical problems, put forward the BIM (building information modeling) analysis technology and building energy consumption are combined, anew design method for energy saving building. Application of BIM technology to create virtual building model contains all the information architecture, the virtual building model into the building energy analysis software, identification, automatic conversion and analyzing a large number of construction data information includes in the model, which is convenient to get the building energy consumption analysis.

An intelligent system for energy management in smart cities based on big data and ontology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zaoui Sayah ◽  
Okba Kazar ◽  
Brahim Lejdel ◽  
Abdelkader Laouid ◽  
Ahmed Ghenabzia
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Energy Saving ◽  
Intelligent System ◽  
Smart Cities ◽  
Semantic Integration ◽  
Saving Energy ◽  
Content Type ◽  
Multi Agent ◽  
Reduce Energy Consumption

PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

scholarly journals HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1097 ◽  
Author(s):  
Isaac Machorro-Cano ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Lisbeth Rodríguez-Mazahua ◽  
José Luis Sánchez-Cervantes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Big Data ◽  
Energy Consumption ◽  
Energy Saving ◽  
Smart Home ◽  
User Preferences ◽  
The Internet ◽  
Automation Systems ◽  
Home Energy Management

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

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