Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing:
As enterprise access networks evolve with a larger number of mobile users, a wide range of devices and new cloud-based applications, managing user performance on an end-to-end basis has become rather challenging. Recent advances in big data network analytics combined with AI and cloud computing are...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Stevenage
The Institution of Engineering and Technology
2022
|
| Schriftenreihe: | IET energy engineering series
213 |
| Online-Zugang: | TUM01 UBY01 Volltext |
| Zusammenfassung: | As enterprise access networks evolve with a larger number of mobile users, a wide range of devices and new cloud-based applications, managing user performance on an end-to-end basis has become rather challenging. Recent advances in big data network analytics combined with AI and cloud computing are being leveraged to tackle this growing problem. AI is becoming further integrated with software that manage networks, storage, and can compute. This edited book focuses on how new network analytics, IoTs and Cloud Computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of service and quality of experience (QoS/QoE) and to improve network performance. From big data and AI analytical techniques for handling the huge amount of data generated by IoT devices, the authors cover cloud storage optimization, the design of next generation access protocols and internet architecture, fault tolerance and reliability in intelligent networks, and discuss a range of emerging applications. This book will be useful to researchers, scientists, engineers, professionals, advanced students and faculty members in ICTs, data science, networking, AI, machine learning and sensing. It will also be of interest to professionals in data science, AI, cloud and IoT start-up companies, as well as developers and designers. |
| Beschreibung: | 1 Online-Ressource (xiii, 409 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781839535345 |
| DOI: | 10.1049/PBPC054E |
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| isbn | 9781839535345 |
| language | English |
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| physical | 1 Online-Ressource (xiii, 409 Seiten) Illustrationen, Diagramme |
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| publishDate | 2022 |
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| publisher | The Institution of Engineering and Technology |
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| series2 | IET energy engineering series |
| spelling | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing edited by Sunil Kumar, Glenford Mapp and Korhan Cengiz Stevenage The Institution of Engineering and Technology 2022 1 Online-Ressource (xiii, 409 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier IET energy engineering series 213 As enterprise access networks evolve with a larger number of mobile users, a wide range of devices and new cloud-based applications, managing user performance on an end-to-end basis has become rather challenging. Recent advances in big data network analytics combined with AI and cloud computing are being leveraged to tackle this growing problem. AI is becoming further integrated with software that manage networks, storage, and can compute. This edited book focuses on how new network analytics, IoTs and Cloud Computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of service and quality of experience (QoS/QoE) and to improve network performance. From big data and AI analytical techniques for handling the huge amount of data generated by IoT devices, the authors cover cloud storage optimization, the design of next generation access protocols and internet architecture, fault tolerance and reliability in intelligent networks, and discuss a range of emerging applications. This book will be useful to researchers, scientists, engineers, professionals, advanced students and faculty members in ICTs, data science, networking, AI, machine learning and sensing. It will also be of interest to professionals in data science, AI, cloud and IoT start-up companies, as well as developers and designers. Kumar, Sunil edt Mapp, Glenford edt Cengiz, Korhan edt Erscheint auch als Druck-Ausgabe 9781839535338 https://doi.org/10.1049/PBPC054E Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing |
| title | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing |
| title_auth | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing |
| title_exact_search | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing |
| title_exact_search_txtP | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing |
| title_full | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing edited by Sunil Kumar, Glenford Mapp and Korhan Cengiz |
| title_fullStr | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing edited by Sunil Kumar, Glenford Mapp and Korhan Cengiz |
| title_full_unstemmed | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing edited by Sunil Kumar, Glenford Mapp and Korhan Cengiz |
| title_short | Intelligent network design driven by Big Data Analytics, IoT, AI and Cloud Computing |
| title_sort | intelligent network design driven by big data analytics iot ai and cloud computing |
| url | https://doi.org/10.1049/PBPC054E |
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