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Ms. Claudia Daniela Antal (Pop)
Technical University of Cluj-Napoca

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Research Keywords & Expertise

1 Distributed Systems
0 Smart Grid
1 Blockchain
0 Ethereum
1 Distributed Ledger Technologies

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Blockchain
Smart Grid
Ethereum
Distributed Systems
Virtual Power Plant

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Journal article
Published: 20 April 2021 in Sensors
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Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.

ACS Style

Marcel Antal; Andrei-Alexandru Cristea; Victor-Alexandru Pădurean; Tudor Cioara; Ionut Anghel; Claudia Antal (Pop); Ioan Salomie; Nicolas Saintherant. Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers. Sensors 2021, 21, 2879 .

AMA Style

Marcel Antal, Andrei-Alexandru Cristea, Victor-Alexandru Pădurean, Tudor Cioara, Ionut Anghel, Claudia Antal (Pop), Ioan Salomie, Nicolas Saintherant. Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers. Sensors. 2021; 21 (8):2879.

Chicago/Turabian Style

Marcel Antal; Andrei-Alexandru Cristea; Victor-Alexandru Pădurean; Tudor Cioara; Ionut Anghel; Claudia Antal (Pop); Ioan Salomie; Nicolas Saintherant. 2021. "Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers." Sensors 21, no. 8: 2879.

Journal article
Published: 22 March 2021 in IEEE Open Journal of the Computer Society
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In the context of COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss how blockchain technology can help in several aspects of COVID-19 vaccination scheme. We present a system in which blockchain technology is used to guaranty data integrity and immutability of beneficiary registration for vaccination, avoiding identity thefts and impersonations. Smart contracts are defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness of all network peers. For vaccine administration, a transparent and tamper-proof solution for side effects self-reporting is provided considering beneficiary and administrated vaccine association. A prototype was implemented using the Ethereum test network, Ropsten, considering the COVID-19 vaccine distribution conditions. The results obtained for each on-chain operation can be checked and validated on the Etherscan. In terms of throughput and scalability, the proposed blockchain system shows promising results while the estimated cost in terms of gas for vaccination scenario based on real data remains within reasonable limits

ACS Style

Claudia Antal; Tudor Cioara; Marcel Antal; Ionut Anghel. Blockchain Platform For COVID-19 Vaccine Supply Management. IEEE Open Journal of the Computer Society 2021, 2, 164 -178.

AMA Style

Claudia Antal, Tudor Cioara, Marcel Antal, Ionut Anghel. Blockchain Platform For COVID-19 Vaccine Supply Management. IEEE Open Journal of the Computer Society. 2021; 2 (99):164-178.

Chicago/Turabian Style

Claudia Antal; Tudor Cioara; Marcel Antal; Ionut Anghel. 2021. "Blockchain Platform For COVID-19 Vaccine Supply Management." IEEE Open Journal of the Computer Society 2, no. 99: 164-178.

Review
Published: 27 February 2021 in Future Internet
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The Distributed Ledger Technology (DLT) provides an infrastructure for developing decentralized applications with no central authority for registering, sharing, and synchronizing transactions on digital assets. In the last years, it has drawn high interest from the academic community, technology developers, and startups mostly by the advent of its most popular type, blockchain technology. In this paper, we provide a comprehensive overview of DLT analyzing the challenges, provided solutions or alternatives, and their usage for developing decentralized applications. We define a three-tier based architecture for DLT applications to systematically classify the technology solutions described in over 100 papers and startup initiatives. Protocol and Network Tier contains solutions for digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scalability and Interoperability Tier solutions address the scalability and interoperability issues with a focus on blockchain technology, where they manifest most often, slowing down its large-scale adoption. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design and implementation of traditional systems.

ACS Style

Claudia Antal; Tudor Cioara; Ionut Anghel; Marcel Antal; Ioan Salomie. Distributed Ledger Technology Review and Decentralized Applications Development Guidelines. Future Internet 2021, 13, 62 .

AMA Style

Claudia Antal, Tudor Cioara, Ionut Anghel, Marcel Antal, Ioan Salomie. Distributed Ledger Technology Review and Decentralized Applications Development Guidelines. Future Internet. 2021; 13 (3):62.

Chicago/Turabian Style

Claudia Antal; Tudor Cioara; Ionut Anghel; Marcel Antal; Ioan Salomie. 2021. "Distributed Ledger Technology Review and Decentralized Applications Development Guidelines." Future Internet 13, no. 3: 62.

Journal article
Published: 12 February 2021 in IEEE Access
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The deployment of small-scale renewable energy sources will transform the management of energy grids towards more decentralized solutions in which the prosumers will have a more active role. Regulatory and market barriers are driving the implementation of virtual aggregation models in which the small-scale prosumers work together on a larger scale to gain benefits that could not be obtained on an individual basis. In this paper, we propose to use public blockchain and self-enforcing smart contracts to construct Virtual Power Plants (VPPs) of prosumers to provide energy services. A model has been defined for capturing the prosumer level constraints in terms of available energy profiles and energy service requirements enabling their optimal aggregation in hierarchical structures. A lightweight decentralized solution for VPPs construction is implemented using smart contracts enabling its efficient running on the public blockchain. Smart contracts are encoding the model constraints and are defining functionalities for prosumers to initiate or join a VPP implementing the complete chain of Offer-Operate-Measure-Remunerate actions. The VPP will be managed on top of a distributed ledger technology offering decentralized functionality for tracking and validating the delivery of energy based on the blockchain transactions and for energy and financial settlement, the remuneration being done according to the amount of energy provided by individual prosumers. Experimental results show that the proposed solution runs successfully on the public blockchain with good execution time and can address Balancing Responsible Party requests for additional generation. The overhead in terms of gas consumption and transactional throughput stays within reasonable boundaries.

ACS Style

Tudor Cioara; Marcel Antal; Vlad T. Mihailescu; Claudia D. Antal; Ionut M. Anghel; Dan Mitrea. Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers. IEEE Access 2021, 9, 29490 -29504.

AMA Style

Tudor Cioara, Marcel Antal, Vlad T. Mihailescu, Claudia D. Antal, Ionut M. Anghel, Dan Mitrea. Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers. IEEE Access. 2021; 9 ():29490-29504.

Chicago/Turabian Style

Tudor Cioara; Marcel Antal; Vlad T. Mihailescu; Claudia D. Antal; Ionut M. Anghel; Dan Mitrea. 2021. "Blockchain-Based Decentralized Virtual Power Plants of Small Prosumers." IEEE Access 9, no. : 29490-29504.

Journal article
Published: 26 November 2020 in Sustainability
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In this paper, we address the management of Data Centers (DCs) by considering their optimal integration with the electrical, thermal, and IT (Information Technology) networks helping them to meet sustainability objectives and gain primary energy savings. Innovative scenarios are defined for exploiting the DCs electrical, thermal, and workload flexibility as a commodity and Information and Communication Technologies (ICT) are proposed and used as enablers for the scenarios’ implementation. The technology and scenarios were evaluated in the context of two operational DCs: a micro DC in Poznan which has on-site renewable sources and a DC in Point Saint Martin. The test cases’ results validate the possibility of using renewable energy sources (RES) for exploiting DCs’ energy flexibility and the potential of combining IT load migration with the availability of RES to increase the amount of energy flexibility by finding a trade-off between the flexibility level, IT load Quality of Service (QoS), and the RES production level. Moreover, the experiments conducted show that the DCs can successfully adapt their thermal energy profile for heat re-use as well as the combined electrical and thermal energy profiles to match specific flexibility requests.

ACS Style

Tudor Cioara; Marcel Antal; Claudia (Pop); Ionut Anghel; Massimo Bertoncini; Diego Arnone; Marilena Lazzaro; Marzia Mammina; Terpsichori-Helen Velivassaki; Artemis Voulkidis; Yoann Ricordel; Nicolas Sainthérant; Ariel Oleksiak; Wojciech Piatek. Data Centers Optimized Integration with Multi-Energy Grids: Test Cases and Results in Operational Environment. Sustainability 2020, 12, 9893 .

AMA Style

Tudor Cioara, Marcel Antal, Claudia (Pop), Ionut Anghel, Massimo Bertoncini, Diego Arnone, Marilena Lazzaro, Marzia Mammina, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Yoann Ricordel, Nicolas Sainthérant, Ariel Oleksiak, Wojciech Piatek. Data Centers Optimized Integration with Multi-Energy Grids: Test Cases and Results in Operational Environment. Sustainability. 2020; 12 (23):9893.

Chicago/Turabian Style

Tudor Cioara; Marcel Antal; Claudia (Pop); Ionut Anghel; Massimo Bertoncini; Diego Arnone; Marilena Lazzaro; Marzia Mammina; Terpsichori-Helen Velivassaki; Artemis Voulkidis; Yoann Ricordel; Nicolas Sainthérant; Ariel Oleksiak; Wojciech Piatek. 2020. "Data Centers Optimized Integration with Multi-Energy Grids: Test Cases and Results in Operational Environment." Sustainability 12, no. 23: 9893.

Journal article
Published: 05 October 2020 in Sensors
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Nowadays, the adoption of demand response programs is still lagging due to the prosumers’ lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of transparency, openness, improved control, and increased active participation of prosumers. Nevertheless, even though in general the transparency of the public blockchain is a desirable feature in the energy domain, the prosumer energy data is sensitive and rather private, thus, a privacy-preserving solution is required. In this paper, we present a decentralized implementation of demand response programs on top of the public blockchain which deals with the privacy of the prosumer’s energy data using zero-knowledge proofs and validates on the blockchain the prosumer’s activity inside the program using smart contracts. Prosumer energy data is kept private, while on the blockchain it is stored a zero-knowledge proof that is generated by the prosumer itself allowing the implementation of functions to validate potential deviations from the request and settle prosumer’s activity. The solution evaluation results are promising in terms of ensuring the privacy of prosumer energy data stored in the public blockchain and detecting potential data inconsistencies.

ACS Style

Claudia Daniela Pop; Marcel Antal; Tudor Cioara; Ionut Anghel; Ioan Salomie. Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy. Sensors 2020, 20, 5678 .

AMA Style

Claudia Daniela Pop, Marcel Antal, Tudor Cioara, Ionut Anghel, Ioan Salomie. Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy. Sensors. 2020; 20 (19):5678.

Chicago/Turabian Style

Claudia Daniela Pop; Marcel Antal; Tudor Cioara; Ionut Anghel; Ioan Salomie. 2020. "Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy." Sensors 20, no. 19: 5678.

Journal article
Published: 27 May 2020 in International Journal of Environmental Research and Public Health
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The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity support in older adults. We describe the architectural views of these services, focusing on details about technology usage, end-user interaction flows and data models that are developed or enhanced to achieve the envisioned objective of healthier, safer, more independent and socially connected older people.

ACS Style

Ionut Anghel; Tudor Cioara; Dorin Moldovan; Claudia Antal; Claudia Daniela Pop; Ioan Salomie; Cristina Bianca Pop; Viorica Rozina Chifu. Smart Environments and Social Robots for Age-Friendly Integrated Care Services. International Journal of Environmental Research and Public Health 2020, 17, 1 .

AMA Style

Ionut Anghel, Tudor Cioara, Dorin Moldovan, Claudia Antal, Claudia Daniela Pop, Ioan Salomie, Cristina Bianca Pop, Viorica Rozina Chifu. Smart Environments and Social Robots for Age-Friendly Integrated Care Services. International Journal of Environmental Research and Public Health. 2020; 17 (11):1.

Chicago/Turabian Style

Ionut Anghel; Tudor Cioara; Dorin Moldovan; Claudia Antal; Claudia Daniela Pop; Ioan Salomie; Cristina Bianca Pop; Viorica Rozina Chifu. 2020. "Smart Environments and Social Robots for Age-Friendly Integrated Care Services." International Journal of Environmental Research and Public Health 17, no. 11: 1.

Journal article
Published: 14 February 2020 in Sustainability
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In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

ACS Style

Andreea Valeria Vesa; Tudor Cioara; Ionut Anghel; Claudia Antal; Claudia Pop; Bogdan Iancu; Ioan Salomie; Vasile Teodor Dadarlat. Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs. Sustainability 2020, 12, 1417 .

AMA Style

Andreea Valeria Vesa, Tudor Cioara, Ionut Anghel, Claudia Antal, Claudia Pop, Bogdan Iancu, Ioan Salomie, Vasile Teodor Dadarlat. Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs. Sustainability. 2020; 12 (4):1417.

Chicago/Turabian Style

Andreea Valeria Vesa; Tudor Cioara; Ionut Anghel; Claudia Antal; Claudia Pop; Bogdan Iancu; Ioan Salomie; Vasile Teodor Dadarlat. 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs." Sustainability 12, no. 4: 1417.

Journal article
Published: 28 October 2019 in Sensors
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Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services act as a barrier for small scale prosumers participation forcing the implementation of new cooperative business models at the local level. This paper is proposing a fog computing infrastructure for the local management of energy systems and the creation of coalitions of prosumers able to provide ancillary services to the grid. It features an edge devices layer for energy monitoring of individual prosumers, a fog layer providing Information and Communication Technologies (ICT) techniques for managing local energy systems by implementing cooperative models, and a cloud layer where the service specific technical requirements are defined. On top, a model has been defined allowing the dynamical construction of coalitions of prosumers as Virtual Power Plants at the fog layer for the provisioning of frequency restoration reserve services while considering both the prosumers' local constraints and the service ones as well as the constituents’ profit maximization. Simulation results show our solution effectiveness in selecting the optimal coalition of prosumers to reliably deliver the service meeting the technical constraints while featuring a low time and computation overhead being feasible to be run closer to the edge.

ACS Style

Claudia Pop; Marcel Antal; Tudor Cioara; Ionut Anghel; Ioan Salomie; Massimo Bertoncini; Pop. A Fog Computing enabled Virtual Power Plant Model for Delivery of Frequency Restoration Reserve Services. Sensors 2019, 19, 4688 .

AMA Style

Claudia Pop, Marcel Antal, Tudor Cioara, Ionut Anghel, Ioan Salomie, Massimo Bertoncini, Pop. A Fog Computing enabled Virtual Power Plant Model for Delivery of Frequency Restoration Reserve Services. Sensors. 2019; 19 (21):4688.

Chicago/Turabian Style

Claudia Pop; Marcel Antal; Tudor Cioara; Ionut Anghel; Ioan Salomie; Massimo Bertoncini; Pop. 2019. "A Fog Computing enabled Virtual Power Plant Model for Delivery of Frequency Restoration Reserve Services." Sensors 19, no. 21: 4688.

Journal article
Published: 10 July 2019 in Sensors
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Nowadays, it has been recognized that blockchain can provide the technological infrastructure for developing decentralized, secure, and reliable smart energy grid management systems. However, an open issue that slows the adoption of blockchain technology in the energy sector is the low scalability and high processing overhead when dealing with the real-time energy data collected by smart energy meters. Thus, in this paper, we propose a scalable second tier solution which combines the blockchain ledger with distributed queuing systems and NoSQL (Not Only SQL database) databases to allow the registration of energy transactions less frequently on the chain without losing the tamper-evident benefits brought by the blockchain technology. At the same time, we propose a technique for tamper-evident registration of smart meters’ energy data and associated energy transactions using digital fingerprinting which allows the energy transaction to be linked hashed-back on-chain, while the sensors data is stored off-chain. A prototype was implemented using Ethereum and smart contracts for the on-chain components while for the off-chain components we used Cassandra database and RabbitMQ messaging broker. The prototype proved to be effective in managing a settlement of energy imbalances use-case and during the evaluation conducted in simulated environment shows promising results in terms of scalability, throughput, and tampering of energy data sampled by smart energy meters.

ACS Style

Claudia Pop; Claudia Antal; Tudor Cioara; Ionut Anghel; David Sera; Ioan Salomie; Giuseppe Raveduto; Denisa Ziu; Vincenzo Croce; Massimo Bertoncini. Blockchain-Based Scalable and Tamper-Evident Solution for Registering Energy Data. Sensors 2019, 19, 3033 .

AMA Style

Claudia Pop, Claudia Antal, Tudor Cioara, Ionut Anghel, David Sera, Ioan Salomie, Giuseppe Raveduto, Denisa Ziu, Vincenzo Croce, Massimo Bertoncini. Blockchain-Based Scalable and Tamper-Evident Solution for Registering Energy Data. Sensors. 2019; 19 (14):3033.

Chicago/Turabian Style

Claudia Pop; Claudia Antal; Tudor Cioara; Ionut Anghel; David Sera; Ioan Salomie; Giuseppe Raveduto; Denisa Ziu; Vincenzo Croce; Massimo Bertoncini. 2019. "Blockchain-Based Scalable and Tamper-Evident Solution for Registering Energy Data." Sensors 19, no. 14: 3033.

Chapter
Published: 26 March 2019 in Computer Aided Verification
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This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.

ACS Style

Aleš Zamuda; Vincenzo Crescimanna; Juan C. Burguillo; Joana Matos Dias; Katarzyna Wegrzyn-Wolska; Imen Rached; Horacio González-Vélez; Roman Senkerik; Claudia Pop; Tudor Cioara; Ioan Salomie; Andrea Bracciali. Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era. Computer Aided Verification 2019, 325 -349.

AMA Style

Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Matos Dias, Katarzyna Wegrzyn-Wolska, Imen Rached, Horacio González-Vélez, Roman Senkerik, Claudia Pop, Tudor Cioara, Ioan Salomie, Andrea Bracciali. Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era. Computer Aided Verification. 2019; ():325-349.

Chicago/Turabian Style

Aleš Zamuda; Vincenzo Crescimanna; Juan C. Burguillo; Joana Matos Dias; Katarzyna Wegrzyn-Wolska; Imen Rached; Horacio González-Vélez; Roman Senkerik; Claudia Pop; Tudor Cioara; Ioan Salomie; Andrea Bracciali. 2019. "Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era." Computer Aided Verification , no. : 325-349.

Journal article
Published: 01 March 2019 in Energies
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This paper addresses the problem of data centers’ cost efficiency considering the potential of reusing the generated heat in district heating networks. We started by analyzing the requirements and heat reuse potential of a high performance computing data center and then we had defined a heat reuse model which simulates the thermodynamic processes from the server room. This allows estimating by means of Computational Fluid Dynamics simulations the temperature of the hot air recovered by the heat pumps from the server room allowing them to operate more efficiently. To address the time and space complexity at run-time we have defined a Multi-Layer Perceptron neural network infrastructure to predict the hot air temperature distribution in the server room from the training data generated by means of simulations. For testing purposes, we have modeled a virtual server room having a volume of 48 m3 and two typical 42U racks. The results show that using our model the heat distribution in the server room can be predicted with an error less than 1 °C allowing data centers to accurately estimate in advance the amount of waste heat to be reused and the efficiency of heat pump operation.

ACS Style

Claudia Antal; Tudor Cioara; Ionut Anghel; Radoslaw Gorzenski; Radoslaw Januszewski; Ariel Oleksiak; Wojciech Piatek; Claudia Pop; Ioan Salomie; Wojciech Szeliga. Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model. Energies 2019, 12, 814 .

AMA Style

Claudia Antal, Tudor Cioara, Ionut Anghel, Radoslaw Gorzenski, Radoslaw Januszewski, Ariel Oleksiak, Wojciech Piatek, Claudia Pop, Ioan Salomie, Wojciech Szeliga. Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model. Energies. 2019; 12 (5):814.

Chicago/Turabian Style

Claudia Antal; Tudor Cioara; Ionut Anghel; Radoslaw Gorzenski; Radoslaw Januszewski; Ariel Oleksiak; Wojciech Piatek; Claudia Pop; Ioan Salomie; Wojciech Szeliga. 2019. "Reuse of Data Center Waste Heat in Nearby Neighborhoods: A Neural Networks-Based Prediction Model." Energies 12, no. 5: 814.

Journal article
Published: 18 December 2018 in Simulation Modelling Practice and Theory
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This paper proposes a methodology for modeling and simulation the operation of Complex Systems considering the energy perspective which may feature both discrete and continuous as well as linear and nonlinear sub-systems. The methodology integrates in a unified view, sub-systems with behavioral or phenomenological models, each of them featuring a set of inputs, outputs and control variables with their associated actions. A proactive optimization process is defined in terms of system level flexibility as well as operation self-adaption, and is leveraging on model simulations to calculate over a time window the system outputs for a set of given inputs and actions. We aim to determine the optimal plan of actions over a set of predicted inputs that will minimize the distance between the system outputs and a given target. To prove its effectiveness, we model and simulate the thermal-electrical processes inside a Data Center, aiming to optimize its operation by exploiting the thermal energy flexibility to re-use the otherwise wasted heat in nearby neighborhoods.

ACS Style

Marcel Antal; Claudia Pop; Teodor Petrican; Andreea Valeria Vesa; Tudor Cioara; Ionut Anghel; Ioan Salomie; Ewa Niewiadomska-Szynkiewicz. MoSiCS: Modeling, simulation and optimization of complex systems–A case study on energy efficient datacenters. Simulation Modelling Practice and Theory 2018, 93, 21 -41.

AMA Style

Marcel Antal, Claudia Pop, Teodor Petrican, Andreea Valeria Vesa, Tudor Cioara, Ionut Anghel, Ioan Salomie, Ewa Niewiadomska-Szynkiewicz. MoSiCS: Modeling, simulation and optimization of complex systems–A case study on energy efficient datacenters. Simulation Modelling Practice and Theory. 2018; 93 ():21-41.

Chicago/Turabian Style

Marcel Antal; Claudia Pop; Teodor Petrican; Andreea Valeria Vesa; Tudor Cioara; Ionut Anghel; Ioan Salomie; Ewa Niewiadomska-Szynkiewicz. 2018. "MoSiCS: Modeling, simulation and optimization of complex systems–A case study on energy efficient datacenters." Simulation Modelling Practice and Theory 93, no. : 21-41.

Conference paper
Published: 01 September 2018 in 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet)
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In this paper we address the problem of classifying the daily life activities of a person out of sensor based monitored data. We propose the use of recurrent neural networks to track of successive sensor data inputs and Long Short-Term Memory cells to address the issues regarding the long-time dependencies in activities' monitored data. The recurrent neural network model was implemented using TensorFlow library. The results are promising showing a mean accuracy of 82.5 using basic cross validation respectively 87.16% using leave one subject out method. Our results are comparable with the ones reported in the state of the art being slightly better in case of the leave one person out validation approach.

ACS Style

Roxana Jurca; Tudor Cioara; Ionut Anghel; Marcel Antal; Claudia Pop; Dorin Moldovan. Activities of Daily Living Classification using Recurrent Neural Networks. 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet) 2018, 1 -4.

AMA Style

Roxana Jurca, Tudor Cioara, Ionut Anghel, Marcel Antal, Claudia Pop, Dorin Moldovan. Activities of Daily Living Classification using Recurrent Neural Networks. 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet). 2018; ():1-4.

Chicago/Turabian Style

Roxana Jurca; Tudor Cioara; Ionut Anghel; Marcel Antal; Claudia Pop; Dorin Moldovan. 2018. "Activities of Daily Living Classification using Recurrent Neural Networks." 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet) , no. : 1-4.

Conference paper
Published: 01 September 2018 in 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)
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This paper tackles the problem of integrating household energy prosumers in Smart Energy Grids by analyzing a set of state-of-the-art energy forecasting techniques that allow individual or aggregated prosumers to evaluate their future energy demand and inform the Distributed System Operator (DSO) about potential grid imbalances. Thus, the DSO can perform a proactive strategy to manage the grid and avoid problems before they appear. The key element of this approach is the prediction technique, that must be accurate enough such that the resulting grid imbalances can be compensated in real-time. The paper evaluates a set of state-of-the-art statistical and Machine Learning (ML) prediction techniques, such as SARIMA, feed-forward and recurrent neural networks, support vector regression or ensemble prediction models, on real household historical energy demand logs by performing a feature selection process for each ML algorithm as to identify the best elements that influence the energy demand of a house. A set of experiments are performed on the REFIT Electrical Load Measurements data set evaluating each model's performance with respect to the selected features. Among the evaluated algorithms, the Ensemble Prediction Model gives best prediction accuracy, showing a Mean Absolute Percentage Error (MAPE) of 14.4% followed by the SVM model with a MAPE of 15.4%.

ACS Style

Teodor Petrican; Andreea Valeria Vesa; Marcel Antal; Claudia Pop; Tudor Cioara; Ionut Anghel; Ioan Salomie. Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids. 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) 2018, 79 -85.

AMA Style

Teodor Petrican, Andreea Valeria Vesa, Marcel Antal, Claudia Pop, Tudor Cioara, Ionut Anghel, Ioan Salomie. Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids. 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP). 2018; ():79-85.

Chicago/Turabian Style

Teodor Petrican; Andreea Valeria Vesa; Marcel Antal; Claudia Pop; Tudor Cioara; Ionut Anghel; Ioan Salomie. 2018. "Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids." 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) , no. : 79-85.

Conference paper
Published: 01 September 2018 in 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)
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This paper addresses the problem of Data Centers (DCs) energy efficiency from a thermal perspective by extending the CloudSim framework to allow simulation and testing of thermal aware resource allocation policies aiming to minimize the cooling system energy consumption. The proposed framework, CoolCloudSim, can be used to develop and test new thermal aware Virtual Machine (VM) allocation strategies aiming to optimize the energy consumption of both cooling system and IT resources while meeting Service Level Agreements (SLAs). The default CloudSim architecture is extended by adding classes which contain mathematical models of the thermal processes within the server room. Furthermore, four new VM allocation policies that consider the cooling system energy consumption are developed based on the thermal and cooling system models. Finally, experiments are run to evaluate various metrics on a set of default CloudSim allocation algorithms and the proposed allocation algorithms. The results show that the proposed algorithms outperform the default CloudSim allocation strategy, Power Aware Best-Fit Decreasing (PABFD), in terms of overall energy consumption and the number of VM migrations, and have on average better results than other existing allocation strategies.

ACS Style

Pintea Cristian; Pintea Eugen; Antal Marcel; Claudia Pop; Tudor Cioara; Ionut Anghel; Ioan Salomie. CoolCloudSim: Integrating Cooling System Models in CloudSim. 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) 2018, 387 -394.

AMA Style

Pintea Cristian, Pintea Eugen, Antal Marcel, Claudia Pop, Tudor Cioara, Ionut Anghel, Ioan Salomie. CoolCloudSim: Integrating Cooling System Models in CloudSim. 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP). 2018; ():387-394.

Chicago/Turabian Style

Pintea Cristian; Pintea Eugen; Antal Marcel; Claudia Pop; Tudor Cioara; Ionut Anghel; Ioan Salomie. 2018. "CoolCloudSim: Integrating Cooling System Models in CloudSim." 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) , no. : 387-394.

Journal article
Published: 11 July 2018 in Information Sciences
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In this paper, we have considered Data Centres (DCs) as computing facilities functioning at the crossroad of electrical, thermal and data networks and have defined optimisation techniques to exploit their energy flexibility. Our methods are leveraging on non-electrical cooling devices such as thermal storage and heat pumps for waste heat reuse and IT workload execution time shifting and spatial relocation in federated DCs. To trade energy flexibility we have defined an Energy Marketplace which allows DCs to act as active energy players integrated into the smart grid, contributing to smart city-level efficiency goals. Reinforcing this vision, we have proposed four innovative business scenarios that enable next generation smart Net-zero Energy DCs acting as energy prosumers at the interface with smart energy grids within smart city environments. Simulation experiments are conducted to determine the DCs potential electrical and thermal energy flexibility in meeting various network level goals and to assess the financial viability of the defined business scenarios. The results show that DCs have a significant amount of energy flexibility which may be shifted and traded to interested stakeholders thus allowing them to gain new revenue streams not foreseen before.

ACS Style

Tudor Cioara; Ionut Anghel; Ioan Salomie; Marcel Antal; Claudia Pop; Massimo Bertoncini; Diego Arnone; Florin Pop. Exploiting data centres energy flexibility in smart cities: Business scenarios. Information Sciences 2018, 476, 392 -412.

AMA Style

Tudor Cioara, Ionut Anghel, Ioan Salomie, Marcel Antal, Claudia Pop, Massimo Bertoncini, Diego Arnone, Florin Pop. Exploiting data centres energy flexibility in smart cities: Business scenarios. Information Sciences. 2018; 476 ():392-412.

Chicago/Turabian Style

Tudor Cioara; Ionut Anghel; Ioan Salomie; Marcel Antal; Claudia Pop; Massimo Bertoncini; Diego Arnone; Florin Pop. 2018. "Exploiting data centres energy flexibility in smart cities: Business scenarios." Information Sciences 476, no. : 392-412.

Conference paper
Published: 01 June 2018 in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
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This paper presents a novel Data Center (DC) flexibility ecosystem, which aims to accelerate the commercial utilization of innovative energy efficiency solutions and on-site integration of renewable energy generation in DCs. The proposed ecosystem leverages on providing mutualized energy flexibility services to power and heat energy grids, while contributing to increased resiliency and security of the power supply.

ACS Style

Ionut Anghel; Tudor Cioara; Claudia Pop; Massimo Bertoncini; Terpsichori-Eleni Velivassaki; Vasiliki Georgiadou; Ariel Oleksiak; Artemis Voulkidis; Nicolas Saintherant; Maria Adele Paglia. Converting Data Centers in Energy Flexibility Ecosystems. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) 2018, 1 -4.

AMA Style

Ionut Anghel, Tudor Cioara, Claudia Pop, Massimo Bertoncini, Terpsichori-Eleni Velivassaki, Vasiliki Georgiadou, Ariel Oleksiak, Artemis Voulkidis, Nicolas Saintherant, Maria Adele Paglia. Converting Data Centers in Energy Flexibility Ecosystems. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). 2018; ():1-4.

Chicago/Turabian Style

Ionut Anghel; Tudor Cioara; Claudia Pop; Massimo Bertoncini; Terpsichori-Eleni Velivassaki; Vasiliki Georgiadou; Ariel Oleksiak; Artemis Voulkidis; Nicolas Saintherant; Maria Adele Paglia. 2018. "Converting Data Centers in Energy Flexibility Ecosystems." 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) , no. : 1-4.

Conference paper
Published: 27 May 2018 in Advances in Intelligent Systems and Computing
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Dementia is an incurable disease that affects a large part of the population of elders and more than 21% of the elders suffering from dementia are exposed to polypharmacy. Moreover, dementia is very correlated with diabetes and high blood pressure. The medication adherence becomes a big challenge that can be approached by analyzing the daily activities of the patients and taking preventive or corrective measures. The weakest link in the pharmacy chain tends to be the patients, especially the patients with cognitive impairments. In this paper we analyze the feasibility of four classification algorithms from the machine learning library of Apache Spark for the prediction of the daily behavior pattern of the patients that suffer from dementia. The algorithms are tested on two datasets from literature that contain data collected from sensors. The best results are obtained when the Random Forest classification algorithm is applied.

ACS Style

Dorin Moldovan; Marcel Antal; Claudia Pop; Adrian Olosutean; Tudor Cioara; Ionut Anghel; Ioan Salomie. Spark-Based Classification Algorithms for Daily Living Activities. Advances in Intelligent Systems and Computing 2018, 69 -78.

AMA Style

Dorin Moldovan, Marcel Antal, Claudia Pop, Adrian Olosutean, Tudor Cioara, Ionut Anghel, Ioan Salomie. Spark-Based Classification Algorithms for Daily Living Activities. Advances in Intelligent Systems and Computing. 2018; ():69-78.

Chicago/Turabian Style

Dorin Moldovan; Marcel Antal; Claudia Pop; Adrian Olosutean; Tudor Cioara; Ionut Anghel; Ioan Salomie. 2018. "Spark-Based Classification Algorithms for Daily Living Activities." Advances in Intelligent Systems and Computing , no. : 69-78.

Journal article
Published: 23 March 2018 in Sustainability
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In this paper, we see the Data Centers (DCs) as producers of waste heat integrated with smart energy infrastructures, heat which can be re-used for nearby neighborhoods. We provide a model of the thermo-electric processes within DCs equipped with heat reuse technology, allowing them to adapt their thermal response profile to meet various levels of hot water demand. On top of the model, we have implemented computational fluid dynamics-based simulations to determine the cooling system operational parameters settings, which allow the heat to build up without endangering the servers’ safety operation as well as the distribution of the workload on the servers to avoid hot spots. This will allow for setting higher temperature set points for short periods of time and using pre-cooling and post-cooling as flexibility mechanisms for DC thermal profile adaptation. To reduce the computational time complexity, we have used neural networks, which are trained using the simulation results. Experiments have been conducted considering a small operational DC featuring a server room of 24 square meters and 60 servers organized in four racks. The results show the DCs’ potential to meet different levels of thermal energy demand by re-using their waste heat in nearby neighborhoods.

ACS Style

Claudia Antal; Tudor Cioara; Ionut Anghel; Claudia Pop; Ioan Salomie. Transforming Data Centers in Active Thermal Energy Players in Nearby Neighborhoods. Sustainability 2018, 10, 939 .

AMA Style

Claudia Antal, Tudor Cioara, Ionut Anghel, Claudia Pop, Ioan Salomie. Transforming Data Centers in Active Thermal Energy Players in Nearby Neighborhoods. Sustainability. 2018; 10 (4):939.

Chicago/Turabian Style

Claudia Antal; Tudor Cioara; Ionut Anghel; Claudia Pop; Ioan Salomie. 2018. "Transforming Data Centers in Active Thermal Energy Players in Nearby Neighborhoods." Sustainability 10, no. 4: 939.