Learning recovery strategies for dynamic self-healing in reactive systems: transport system implementation with dynamic predicates
Self-healing systems are advanced tools in modern system management, designed to detect and resolve issues automatically, ensuring continuous operation without manual intervention. Leveraging adaptive algorithms and real-time data analysis, these systems identify anomalies, apply corrective measures...
- Autores:
-
Dussán Rueda, Luis Felipe
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/75259
- Acceso en línea:
- https://hdl.handle.net/1992/75259
- Palabra clave:
- SelfHealing
Systems
Predicates
Dynamic
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| Summary: | Self-healing systems are advanced tools in modern system management, designed to detect and resolve issues automatically, ensuring continuous operation without manual intervention. Leveraging adaptive algorithms and real-time data analysis, these systems identify anomalies, apply corrective measures, and maintain optimal performance and resilience. They dynamically adapt to changing conditions, reducing downtime and enhancing reliability, making them essential for managing complex, dynamic environments where traditional static monitoring falls short. This thesis examines self-healing systems within the Transport System Implementation framework, emphasizing dynamic predicates and Reinforcement Learning to enhance adaptability and responsiveness. Through analysis and simulation, it highlights their benefits and potential applications across various domains. |
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