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Centralized Monitoring Infrastructure on Cloud: An Open Source Approach.

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Centralized Monitoring Infrastructure on Cloud: An Open Source Approach.

Rel. Daniele Apiletti. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024

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Abstract:

In the contemporary digital landscape, technology and data processing play pivotal roles, significantly influencing diverse facets of our lives, particularly within the realm of IT firms. Efficiently managing vast amounts of data requires solid architectures, especially as modern data complexities demand advanced solutions. This thesis explores the details of monitoring resources and detecting issues in system management. It addresses challenges such as scalability, security issues, and the complexities of centralized architecture, going beyond specific company contexts. The goal is to contribute by creating an open-source alternative to Oracle OMS and Oracle Enterprise Manager systems, emphasizing accessibility and scalability. This choice emphasizes enhancing accessibility and scalability, promoting transparency, collaboration, and adaptability in resource monitoring and issue detection within system management. The first part emphasizes architecture management, honing in on the utilization of time series databases. Integrating remote write capabilities enhances system flexibility, enabling smooth data transfer, real-time collaboration, and efficient management of decentralized sources. This not only optimizes data handling but also fosters interoperability in diverse environments. The second part focuses on comprehensive data management and visualization within the proposed alternative solution. It scrutinizes the efficacy of intuitive dashboards for visually representing intricate datasets, providing users with a dynamic and user-friendly interface for seamless interaction. This dual-part exploration seeks to address the complexities in system management and contribute to the development of a scalable, accessible, and efficient monitoring solution.

Relators: Daniele Apiletti
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 73
Subjects:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: Mediamente Consulting srl
URI: http://webthesis.biblio.polito.it/id/eprint/30849
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