High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining...

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Veröffentlicht in:	Cluster computing Jg. 26; H. 1; S. 495 - 512
Hauptverfasser:	Abbasi, Mahdi, Najafabadi, Azam Fazel, Elghali, Seifeddine Ben, Zerrougui, Mohamed, Khosravi, Mohammad R., Nasser, Habib
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2023 Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Central processing units Classification Clusters Computer Communication Networks Computer memory Computer Science CPUs Decision making Decision trees Decomposition Electricity Energy consumption Energy sources Energy storage Engineering Sciences Libraries Measuring instruments Methods Microprocessors New technology Operating Systems Packets (communication) Parallel processing Personal information Power plants Processor Architectures Renewable resources Software Strategic management Virtual power plants Anonymization Virtual power plant (VPP) Tuple space algorithm CPU cluster OpenMP Flow classification MPI
ISSN:	1386-7857, 1573-7543
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Abstract	The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved.
AbstractList	The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved.
Author	Abbasi, Mahdi Nasser, Habib Najafabadi, Azam Fazel Elghali, Seifeddine Ben Khosravi, Mohammad R. Zerrougui, Mohamed
Author_xml	– sequence: 1 givenname: Mahdi orcidid: 0000-0002-5373-5778 surname: Abbasi fullname: Abbasi, Mahdi email: abbasi@basu.ac.ir, mahdi-abbasi@lis-lab.fr organization: Department of Computer Engineering, Engineering Faculty, Bu-Ali Sina University, Aix Marseille Univ, Université de Toulon, CNRS, LIS – sequence: 2 givenname: Azam Fazel surname: Najafabadi fullname: Najafabadi, Azam Fazel organization: Department of Computer Engineering, Engineering Faculty, Bu-Ali Sina University – sequence: 3 givenname: Seifeddine Ben surname: Elghali fullname: Elghali, Seifeddine Ben organization: Aix Marseille Univ, Université de Toulon, CNRS, LIS – sequence: 4 givenname: Mohamed surname: Zerrougui fullname: Zerrougui, Mohamed organization: Aix Marseille Univ, Université de Toulon, CNRS, LIS – sequence: 5 givenname: Mohammad R. surname: Khosravi fullname: Khosravi, Mohammad R. organization: Department of Computer Engineering, Engineering Faculty, Bu-Ali Sina University – sequence: 6 givenname: Habib surname: Nasser fullname: Nasser, Habib organization: RDI’UP (Innovative Research)
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Keywords	Anonymization Virtual power plant (VPP) Tuple space algorithm CPU cluster OpenMP Flow classification MPI
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SubjectTerms	Algorithms Central processing units Classification Clusters Computer Communication Networks Computer memory Computer Science CPUs Decision making Decision trees Decomposition Electricity Energy consumption Energy sources Energy storage Engineering Sciences Libraries Measuring instruments Methods Microprocessors New technology Operating Systems Packets (communication) Parallel processing Personal information Power plants Processor Architectures Renewable resources Software Strategic management Virtual power plants
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Title	High-performance pseudo-anonymization of virtual power plant data on a CPU cluster
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