Abstract
Managing a multi-tenant network presents formidable challenges to managed service providers (MSPs) as they endeavour to provide exceptional service quality to clients scattered across diverse locations. These challenges are compounded by clients' stringent requirements to shield the identities of their network elements, preventing their exposure for centralised monitoring purposes. Consequently, the task of MSPs in overseeing geographically dispersed networks becomes more intricate due to the necessity of establishing separate real-time monitoring systems for each client. Moreover, the importance of visualising monitored data cannot be overstated, as it unveils invaluable patterns that illuminate service quality issues. Consequently, a monitoring tool equipped with a richly informative dashboard becomes indispensable for strategic planning and the delivery of top-tier services. Furthermore, the integration of AI -driven resource prediction enhances incident resolution capabilities by enabling proactive alert notifications. In response to these complex challenges, this research introduces an architectural solution centred around a data visualisation platform. This platform harnesses the power of a portable, decentralised AI-enabled real-time network monitoring tool custom-tailored for multi-tenant networks. The proposed approach adopts a pragmatic strategy that seamlessly integrates multiple open-source modules across geographically dispersed networks. The result is nothing short of remarkable compared with the manual approach, with network element downtime experiencing a remarkable 95% reduction, and incident resolution seeing a noteworthy 90 % reduction. This transformative impact directly benefits service desk professionals and network design engineers alike of MSPs.
Original language | English |
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Title of host publication | Big Data and Smart Computing Conference 2024 |
Editors | Herwig Unger, Jinseok Chae, Young-Koo Lee, Christian Wagner, Chaokun Wang, Mehdi Bennis, Mahasak Ketcham, Young-Kyoon Suh, Hyuk-Yoon Kwon |
Publisher | IEEE |
Pages | 124-130 |
Number of pages | 7 |
ISBN (Electronic) | 9798350370027 |
ISBN (Print) | 9798350370034 |
DOIs | |
Publication status | Published - 18 Feb 2024 |
Event | 2024 IEEE International Conference on Big Data and Smart Computing - The Sukosol Hotel, Bangkok, Thailand Duration: 18 Feb 2024 → 21 Feb 2024 https://www.bigcomputing.org/conf2024/ |
Publication series
Name | IEEE Conference Proceedings |
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Publisher | IEEE |
ISSN (Print) | 2375-933X |
ISSN (Electronic) | 2375-9356 |
Conference
Conference | 2024 IEEE International Conference on Big Data and Smart Computing |
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Abbreviated title | BIGCOMP |
Country/Territory | Thailand |
City | Bangkok |
Period | 18/02/24 → 21/02/24 |
Internet address |
Keywords
- multi-tenant network
- monitoring
- decentralisation
- machine learning
- artificial intelligence