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Éric Rondeau
Université de Lorraine, CNRS, CRAN, F-54000, France

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Journal article
Published: 13 May 2021 in Journal of Cleaner Production
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The residential sector accounts for 30% of the total green house gas emissions in Europe, which can be reduced either by switching to low-carbon technologies or reducing the amount of fossil fuel energy consumed. In this work, a new greenhouse gas emission (GHGE) reduction system at the house (nanogrid) level is investigated. The originality of the proposed system and underlying algorithm lies in the fact that it acts in a proactive manner, by continuously controlling and optimizing energy flows between on-site local power production systems (photovoltaics - PV - array in our case), loads, and storage units (combining battery and thermal storage reservoirs). This system/algorithm is evaluated based on real-life input datasets from the United Kingdom (UK) and France, and compared with traditional house energy infrastructures, namely (i) a house not fitted with battery, and (ii) a house fitted with battery but without additional “smart” software layer. Results show that it performs better in terms of CO2 (capacity of the algorithm to reduce the amount of non carbon-free energy consumed from the grid), Power to Grid (capacity to maximize the use of local green energy), and financial cost (capacity to reduce the overall electricity bill), respectively improving performance by up to 8%, 10% and 37%.

ACS Style

Paul Ortiz; Sylvain Kubler; Éric Rondeau; Jean-Philippe Georges; Giuseppe Colantuono; Alexander Alexandrovich Shukhobodskiy. Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs. Journal of Cleaner Production 2021, 310, 127347 .

AMA Style

Paul Ortiz, Sylvain Kubler, Éric Rondeau, Jean-Philippe Georges, Giuseppe Colantuono, Alexander Alexandrovich Shukhobodskiy. Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs. Journal of Cleaner Production. 2021; 310 ():127347.

Chicago/Turabian Style

Paul Ortiz; Sylvain Kubler; Éric Rondeau; Jean-Philippe Georges; Giuseppe Colantuono; Alexander Alexandrovich Shukhobodskiy. 2021. "Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs." Journal of Cleaner Production 310, no. : 127347.

Journal article
Published: 07 November 2020 in Sustainability
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Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

ACS Style

Madhubala Ganesan; Ah-Lian Kor; Colin Pattinson; Eric Rondeau. Green Cloud Software Engineering for Big Data Processing. Sustainability 2020, 12, 9255 .

AMA Style

Madhubala Ganesan, Ah-Lian Kor, Colin Pattinson, Eric Rondeau. Green Cloud Software Engineering for Big Data Processing. Sustainability. 2020; 12 (21):9255.

Chicago/Turabian Style

Madhubala Ganesan; Ah-Lian Kor; Colin Pattinson; Eric Rondeau. 2020. "Green Cloud Software Engineering for Big Data Processing." Sustainability 12, no. 21: 9255.

Journal article
Published: 25 August 2020 in Energies
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The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data.

ACS Style

Anastasiia Grishina; Marta Chinnici; Ah-Lian Kor; Eric Rondeau; Jean-Philippe Georges. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. Energies 2020, 13, 4378 .

AMA Style

Anastasiia Grishina, Marta Chinnici, Ah-Lian Kor, Eric Rondeau, Jean-Philippe Georges. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. Energies. 2020; 13 (17):4378.

Chicago/Turabian Style

Anastasiia Grishina; Marta Chinnici; Ah-Lian Kor; Eric Rondeau; Jean-Philippe Georges. 2020. "A Machine Learning Solution for Data Center Thermal Characteristics Analysis." Energies 13, no. 17: 4378.

Preprint
Published: 15 July 2020
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Energy efficiency of Data Center (DC) operations heavily relies on IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of IoT- enabled smart systems. Consequently, increased demand for computing power will bring about increased waste heat dissipation in data centers. In order to bring about a DC energy efficiency, it is imperative to explore the thermal characteristics analysis of an IT room (due to waste heat). This work encompasses the employment of an unsupervised machine learning modelling technique for uncovering weaknesses of the DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for energy efficiency improvement that will feed into DC recommendations. The methodology employed for this research includes statistical analysis of IT room thermal characteristics, and the identification of individual servers that frequently occur in the hotspot zones. A critical analysis has been conducted on available big dataset of ambient air temperature in the hot aisle of ENEA Portici CRESCO6 computing cluster. Clustering techniques have been used for hotspots localization as well as categorization of nodes based on surrounding air temperature ranges. The principles and approaches covered in this work are replicable for energy efficiency evaluation of any DC and thus, foster transferability. This work showcases applicability of best practices and guidelines in the context of a real commercial DC that transcends the set of existing metrics for DC energy efficiency assessment.

ACS Style

Marta Chinnici; Anastasiia Grishina; Ah-Lian Kor; Eric Rondeau; Jean Philippe Georges. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. 2020, 1 .

AMA Style

Marta Chinnici, Anastasiia Grishina, Ah-Lian Kor, Eric Rondeau, Jean Philippe Georges. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. . 2020; ():1.

Chicago/Turabian Style

Marta Chinnici; Anastasiia Grishina; Ah-Lian Kor; Eric Rondeau; Jean Philippe Georges. 2020. "A Machine Learning Solution for Data Center Thermal Characteristics Analysis." , no. : 1.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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Several works have been done in order to balance the energy consumption of the network with traffic which aims to have a positive impact on the CO2 emission. However, CO2 and energy consumption cannot be considered proportionate if the means of electricity production differs. In this paper, we have proposed two different metrics namely Carbon Emission Factor and Non-Renewable Energy usage percentage for achieving green network. We have designed an algorithm considering these metrics as objective functions. We have considered a software defined network approach and provided a set of data and control plane for each metric. Their performances are then analyzed and compared with respect to green policy enabled Shortest Path First algorithm. All the experiments are conducted on GÉANT network with realistic demand size. A comprehensive analysis of the quality of service parameters like the end to end delay and packet loss is also done for each metric of the algorithm.

ACS Style

Mohaimenul Hossain; Jean-Philippe Georges; Eric Rondeau; Thierry Divoux. Using SDN for Controlling the Carbon Footprint of The Internet. IFAC-PapersOnLine 2020, 53, 8303 -8308.

AMA Style

Mohaimenul Hossain, Jean-Philippe Georges, Eric Rondeau, Thierry Divoux. Using SDN for Controlling the Carbon Footprint of The Internet. IFAC-PapersOnLine. 2020; 53 (2):8303-8308.

Chicago/Turabian Style

Mohaimenul Hossain; Jean-Philippe Georges; Eric Rondeau; Thierry Divoux. 2020. "Using SDN for Controlling the Carbon Footprint of The Internet." IFAC-PapersOnLine 53, no. 2: 8303-8308.

Journal article
Published: 30 June 2019 in Sensors
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There are all sort of indications that Internet usage will go only upwards, resulting in an increase in energy consumption and CO2 emissions. At the same time, a significant amount of this carbon footprint corresponds to the information and communication technologies (ICT) sector, with around one third being due to networking. In this paper we have approached the problem of green networking from the point of view of sustainability. Here, alongside energy-aware routing, we have also introduced pollution-aware routing with environmental metrics like carbon emission factor and non-renewable energy usage percentage. We have proposed an algorithm based on these three candidate-metrics. Our algorithm provides optimum data and control planes for three different metrics which regulate the usage of different routers and adapt the bandwidth of the links while giving the traffic demand requirements utmost priority. We have made a comparison between these three metrics in order to show their impact on greening routing. The results show that for a particular scenario, our pollution-aware routing algorithm can reduce 36% and 20% of CO2 emissions compared to shortest path first and energy-based solutions, respectively.

ACS Style

Mohaimenul Hossain; Jean-Philippe Georges; Eric Rondeau; Thierry Divoux. Energy, Carbon and Renewable Energy: Candidate Metrics for Green-aware Routing? Sensors 2019, 19, 2901 .

AMA Style

Mohaimenul Hossain, Jean-Philippe Georges, Eric Rondeau, Thierry Divoux. Energy, Carbon and Renewable Energy: Candidate Metrics for Green-aware Routing? Sensors. 2019; 19 (13):2901.

Chicago/Turabian Style

Mohaimenul Hossain; Jean-Philippe Georges; Eric Rondeau; Thierry Divoux. 2019. "Energy, Carbon and Renewable Energy: Candidate Metrics for Green-aware Routing?" Sensors 19, no. 13: 2901.

Journal article
Published: 22 May 2019 in Journal of Cleaner Production
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Data centers are estimated to have the fastest growing carbon footprint from across the whole information and communication technology (ICT) sector. Evaluating the performance of data centers in terms of energy efficiency and sustainability is becoming an increasingly important matter for organizations and governments (e.g., for regulation or reputation purposes). It nonetheless remains difficult to achieve such evaluation, as data centers imply to take into consideration a wide range of dimensions and stakeholders. Even though a wide range of sustainability performance indicators exist in the literature, there is still a lack of frameworks to help data center stakeholders (spanning from data center owners, governmental regulators to engineers/field operators) to evaluate and understand how a data center performs in terms of sustainable development/behavior. Our research work proposes such a framework, whose originality lies in the combination of state-of-the-art sustainability metrics with the biomimicry commandments of eco-mature system, which enables holistic sustainability assessment of data centres. From a theoretical perspective, the proposed model is designed based on a benefit-cost analysis using the Analytic Hierarchy Process (AHP) technique. This approach allows data center stakeholders for specifying their own preferences and/or expertise in the comparison process, whose practicability is demonstrated in this paper considering three data center candidates, which are respectively located in France, Germany and Sweden.

ACS Style

Sylvain Kubler; Éric Rondeau; Jean-Philippe Georges; Phoebe Lembi Mutua; Marta Chinnici. Benefit-cost model for comparing data center performance from a biomimicry perspective. Journal of Cleaner Production 2019, 231, 817 -834.

AMA Style

Sylvain Kubler, Éric Rondeau, Jean-Philippe Georges, Phoebe Lembi Mutua, Marta Chinnici. Benefit-cost model for comparing data center performance from a biomimicry perspective. Journal of Cleaner Production. 2019; 231 ():817-834.

Chicago/Turabian Style

Sylvain Kubler; Éric Rondeau; Jean-Philippe Georges; Phoebe Lembi Mutua; Marta Chinnici. 2019. "Benefit-cost model for comparing data center performance from a biomimicry perspective." Journal of Cleaner Production 231, no. : 817-834.

Journal article
Published: 01 March 2016 in Future Generation Computer Systems
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A new Internet of Things area is coming with communicating materials, which are able to provide diverse functionalities to users all along the product lifecycle. As example, it can track its own evolution which leads to gather helpful information. This new paradigm is fulfilled via the integration of specific electronic components into the product material. In this work, ultra-small Wireless Sensor Networks (WSN) are used for large scale materials such as concrete in smart building. Indeed, storage of lifecycle information and data dissemination in communicating materials are very important issues. Therefore, this paper provides solution for storing data by systematic dissemination through the integrated WSN. It presents USEE, a uniform data storage protocol for large scale communicating material. USEE guarantees that information could be retrieved in each piece of the material by intelligently managing data replication among each neighborhood of the WSN. Unlike related protocols of the literature, USEE considers in the same set uniformity storage in the whole network, the data importance level, and the resource constraints of sensor nodes. When compared with related protocols such as RaWMS, DEEP, and Supple, USEE shows a uniform dissemination and low communication overhead tradeoff for all the data importance levels.

ACS Style

Kais Mekki; Ahmed Zouinkhi; William Derigent; Eric Rondeau; André Thomas; Mohamed Naceur Abdelkrim. USEE: A uniform data dissemination and energy efficient protocol for communicating materials. Future Generation Computer Systems 2016, 56, 651 -663.

AMA Style

Kais Mekki, Ahmed Zouinkhi, William Derigent, Eric Rondeau, André Thomas, Mohamed Naceur Abdelkrim. USEE: A uniform data dissemination and energy efficient protocol for communicating materials. Future Generation Computer Systems. 2016; 56 ():651-663.

Chicago/Turabian Style

Kais Mekki; Ahmed Zouinkhi; William Derigent; Eric Rondeau; André Thomas; Mohamed Naceur Abdelkrim. 2016. "USEE: A uniform data dissemination and energy efficient protocol for communicating materials." Future Generation Computer Systems 56, no. : 651-663.

Journal article
Published: 31 October 2013 in Computers & Industrial Engineering
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In recent years, some scholars claimed the usage of intelligent products to make systems more efficient throughout the Product Life Cycle (PLC). Integrating intelligence and information into products themselves is possible with, among others, auto-ID technologies (barcode, RFID, …). In this paper, a new kind of intelligent product is introduced, referred to as “communicating material” paradigm. Through this paradigm, a product is (i) capable of embedding information on all or parts of the material that it is made of and (ii) capable of undergoing physical transformations without losing its communication ability and the data that is stored on it. This new material is used in our study to convey information between the different actors of the PLC, thus improving data interoperability, availability and sustainability. Although “communicating materials” provide new abilities compared to conventional products, they still have low memory capacities compared to product databases that become larger and larger. An information dissemination framework is developed in this paper to select the appropriate information to be stored on the product, at different stages of the PLC. This appropriateness is based on a degree of data relevance, which is computed by taking into account the context of use of the product (actor’s expectations, environment, …). This framework also provides the tools to split information on all or parts of the material. A case study is presented, which aims at embedding context-sensitive information on “communicating textiles”.

ACS Style

Sylvain Kubler; William Derigent; Eric Rondeau; André Thomas; Kary Främling. Information dissemination framework for context-aware products. Computers & Industrial Engineering 2013, 66, 485 -500.

AMA Style

Sylvain Kubler, William Derigent, Eric Rondeau, André Thomas, Kary Främling. Information dissemination framework for context-aware products. Computers & Industrial Engineering. 2013; 66 (2):485-500.

Chicago/Turabian Style

Sylvain Kubler; William Derigent; Eric Rondeau; André Thomas; Kary Främling. 2013. "Information dissemination framework for context-aware products." Computers & Industrial Engineering 66, no. 2: 485-500.

Journal article
Published: 01 January 2011 in Wireless Sensor Network
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WSNs are designed to efficiently collect data and monitor environments, among other applications. This article describes the concept and realization of an Active Security System for security management of warehousing of chemical substances using WSNs. We present an approach to modeling and simulating cooperation between intelligent products that are equipped with a platform of sensor networks and ambient communication capabilities to increase their security, in a context of ambient intelligence of a deposit for chemical substances. Behavior evolution of every intelligent product is modeled by hierarchical Petri Nets. The simulation of the model is implemented in the Castalia-OMNET++ Tools language.

ACS Style

Ahmed Zouinkhi; Eddy Bajic; Eric Rondeau; Mohamed Ben Gayed; Mohamed Naceur Abdelkrim. Ambient Intelligence: Awareness Context Application in Industrial Storage. Wireless Sensor Network 2011, 03, 135 -146.

AMA Style

Ahmed Zouinkhi, Eddy Bajic, Eric Rondeau, Mohamed Ben Gayed, Mohamed Naceur Abdelkrim. Ambient Intelligence: Awareness Context Application in Industrial Storage. Wireless Sensor Network. 2011; 03 (04):135-146.

Chicago/Turabian Style

Ahmed Zouinkhi; Eddy Bajic; Eric Rondeau; Mohamed Ben Gayed; Mohamed Naceur Abdelkrim. 2011. "Ambient Intelligence: Awareness Context Application in Industrial Storage." Wireless Sensor Network 03, no. 04: 135-146.

Book chapter
Published: 27 September 2010 in Advances in Petri Net Theory and Applications
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Petri Nets Hierarchical Modelling Framework of Active Products' Community | InTechOpen, Published on: 2010-09-27. Authors: Ahmed Zouinkhi, Eddy Bajic, Eric Rondeau, et

ACS Style

Ahmed Zouinkhi; Eddy Bajic; Eric Rondeau; Mohamed Naceur. Petri Nets Hierarchical Modelling Framework of Active Products' Community. Advances in Petri Net Theory and Applications 2010, 1 .

AMA Style

Ahmed Zouinkhi, Eddy Bajic, Eric Rondeau, Mohamed Naceur. Petri Nets Hierarchical Modelling Framework of Active Products' Community. Advances in Petri Net Theory and Applications. 2010; ():1.

Chicago/Turabian Style

Ahmed Zouinkhi; Eddy Bajic; Eric Rondeau; Mohamed Naceur. 2010. "Petri Nets Hierarchical Modelling Framework of Active Products' Community." Advances in Petri Net Theory and Applications , no. : 1.