An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS
Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic plann...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huo, Liuwei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Mobile networks and applications - Springer US, 1996, 26(2019), 2 vom: 10. Dez., Seite 575-585 |
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| Übergeordnetes Werk: |
volume:26 ; year:2019 ; number:2 ; day:10 ; month:12 ; pages:575-585 |
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| DOI / URN: |
10.1007/s11036-019-01419-z |
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| 520 | |a Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. | ||
| 650 | 4 | |a Enterprise information system | |
| 650 | 4 | |a Software-defined networking | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Adaptive network measurement | |
| 650 | 4 | |a Measurement primitives | |
| 700 | 1 | |a Jiang, Dingde |4 aut | |
| 700 | 1 | |a Qi, Sheng |4 aut | |
| 700 | 1 | |a Song, Houbing |4 aut | |
| 700 | 1 | |a Miao, Lei |4 aut | |
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10.1007/s11036-019-01419-z doi (DE-627)OLC2125370913 (DE-He213)s11036-019-01419-z-p DE-627 ger DE-627 rakwb eng 004 VZ Huo, Liuwei verfasserin aut An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. Enterprise information system Software-defined networking Artificial intelligence Adaptive network measurement Measurement primitives Jiang, Dingde aut Qi, Sheng aut Song, Houbing aut Miao, Lei aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 10. Dez., Seite 575-585 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:10 month:12 pages:575-585 https://doi.org/10.1007/s11036-019-01419-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 10 12 575-585 |
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10.1007/s11036-019-01419-z doi (DE-627)OLC2125370913 (DE-He213)s11036-019-01419-z-p DE-627 ger DE-627 rakwb eng 004 VZ Huo, Liuwei verfasserin aut An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. Enterprise information system Software-defined networking Artificial intelligence Adaptive network measurement Measurement primitives Jiang, Dingde aut Qi, Sheng aut Song, Houbing aut Miao, Lei aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 10. Dez., Seite 575-585 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:10 month:12 pages:575-585 https://doi.org/10.1007/s11036-019-01419-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 10 12 575-585 |
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10.1007/s11036-019-01419-z doi (DE-627)OLC2125370913 (DE-He213)s11036-019-01419-z-p DE-627 ger DE-627 rakwb eng 004 VZ Huo, Liuwei verfasserin aut An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. Enterprise information system Software-defined networking Artificial intelligence Adaptive network measurement Measurement primitives Jiang, Dingde aut Qi, Sheng aut Song, Houbing aut Miao, Lei aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 10. Dez., Seite 575-585 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:10 month:12 pages:575-585 https://doi.org/10.1007/s11036-019-01419-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 10 12 575-585 |
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10.1007/s11036-019-01419-z doi (DE-627)OLC2125370913 (DE-He213)s11036-019-01419-z-p DE-627 ger DE-627 rakwb eng 004 VZ Huo, Liuwei verfasserin aut An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. Enterprise information system Software-defined networking Artificial intelligence Adaptive network measurement Measurement primitives Jiang, Dingde aut Qi, Sheng aut Song, Houbing aut Miao, Lei aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 10. Dez., Seite 575-585 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:10 month:12 pages:575-585 https://doi.org/10.1007/s11036-019-01419-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 10 12 575-585 |
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10.1007/s11036-019-01419-z doi (DE-627)OLC2125370913 (DE-He213)s11036-019-01419-z-p DE-627 ger DE-627 rakwb eng 004 VZ Huo, Liuwei verfasserin aut An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. Enterprise information system Software-defined networking Artificial intelligence Adaptive network measurement Measurement primitives Jiang, Dingde aut Qi, Sheng aut Song, Houbing aut Miao, Lei aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 10. Dez., Seite 575-585 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:10 month:12 pages:575-585 https://doi.org/10.1007/s11036-019-01419-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 10 12 575-585 |
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Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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An AI-Based Adaptive Cognitive Modeling and Measurement Method of Network Traffic for EIS |
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Jiang, Dingde Qi, Sheng Song, Houbing Miao, Lei |
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Jiang, Dingde Qi, Sheng Song, Houbing Miao, Lei |
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2024-07-04T03:27:48.928Z |
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