Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments
The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this pa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/23/4037 |
| _version_ | 1797463348507312128 |
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| author | Sungju Lee Taikyeong Jeong |
| author_facet | Sungju Lee Taikyeong Jeong |
| author_sort | Sungju Lee |
| collection | DOAJ |
| description | The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose two techniques for real-time interconnection and optimal resource allocation for a large-scale distributed system. In particular, to configure a CMP, we analyze the data transfer rate and utilization rate from the intelligent electronic device (IED), collecting the power production data to the individual controller. The details provided in this paper are used to design a sample value, i.e., raw data transfer, on the basis of the IEC 61850 protocol for mapping. The choice of sampled values is to attain the critical time requirement, data transmission of current transformers, voltage transformers, and protective relaying of less than 1 s without complicating the real-time implementation. Furthermore, in this paper, a way to determine the optimal number of physical resources (i.e., CPU, memory, and network) for a given system is discussed. CPU ranged from 0.9 to 0.98 while each cluster increased from 10 to 1000. With the same condition, memory utilized almost 100% utilization from 0.98 to 1. Lastly, the network utilization rate was 0.96 and peaked at 1 at most. Based on the results, we confirm that a large-scale distributed system can provide a seamless monitoring service to distribute messages for each IED, and this can provide a configuration for CMP without exceeding 100% utilization. |
| first_indexed | 2024-03-09T17:49:22Z |
| format | Article |
| id | doaj.art-68322d8c80094a3bbb15345e0effc51b |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:49:22Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-68322d8c80094a3bbb15345e0effc51b2023-11-24T10:49:36ZengMDPI AGElectronics2079-92922022-12-011123403710.3390/electronics11234037Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and EnvironmentsSungju Lee0Taikyeong Jeong1Department of Software, Sangmyung University, Cheonan-si 31066, Dongnam-gu, Republic of KoreaSchool of Artificial Intelligence Convergence, Hallym University, Chuncheon-si 24252, Gangwon-do, Republic of KoreaThe demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose two techniques for real-time interconnection and optimal resource allocation for a large-scale distributed system. In particular, to configure a CMP, we analyze the data transfer rate and utilization rate from the intelligent electronic device (IED), collecting the power production data to the individual controller. The details provided in this paper are used to design a sample value, i.e., raw data transfer, on the basis of the IEC 61850 protocol for mapping. The choice of sampled values is to attain the critical time requirement, data transmission of current transformers, voltage transformers, and protective relaying of less than 1 s without complicating the real-time implementation. Furthermore, in this paper, a way to determine the optimal number of physical resources (i.e., CPU, memory, and network) for a given system is discussed. CPU ranged from 0.9 to 0.98 while each cluster increased from 10 to 1000. With the same condition, memory utilized almost 100% utilization from 0.98 to 1. Lastly, the network utilization rate was 0.96 and peaked at 1 at most. Based on the results, we confirm that a large-scale distributed system can provide a seamless monitoring service to distribute messages for each IED, and this can provide a configuration for CMP without exceeding 100% utilization.https://www.mdpi.com/2079-9292/11/23/4037large-scale distributed systemcluster servicesreal-timemonitoring |
| spellingShingle | Sungju Lee Taikyeong Jeong Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments Electronics large-scale distributed system cluster services real-time monitoring |
| title | Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments |
| title_full | Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments |
| title_fullStr | Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments |
| title_full_unstemmed | Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments |
| title_short | Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments |
| title_sort | large scale distributed system and design methodology for real time cluster services and environments |
| topic | large-scale distributed system cluster services real-time monitoring |
| url | https://www.mdpi.com/2079-9292/11/23/4037 |
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