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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Vydáno v:Cluster computing Ročník 26; číslo 1; s. 495 - 512
Hlavní autoři: Abbasi, Mahdi, Najafabadi, Azam Fazel, Elghali, Seifeddine Ben, Zerrougui, Mohamed, Khosravi, Mohammad R., Nasser, Habib
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2023
Springer Nature B.V
Springer Verlag
Témata:
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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Shrnutí: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.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-021-03526-7