Angelo Gennuso
Breaking the Challenge of Smart Microservice Autoscaling through Coordination.
Rel. Maurizio Morisio. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (8MB) | Preview |
Abstract
In recent years, microservice-based architectures (MSA) have gathered con- siderable attention for their potential to revolutionize the design and deployment of large-scale applications. This model promotes flexible, loosely coupled, and finely engineered software, making them easier to manage and facilitating DevOps practices. A critical aspect of MSA is the efficient scaling of microservices while ef- fectively managing resource allocation in the midst of increasing load intensity. Scaling may be applied vertically, by adding more resources to individual mi- croservices, horizontally, by instantiating additional instances of congested mi- croservices, or both at the same time. Recent works have investigated machine learning techniques and, in particular, reinforcement learning (RL) [1] [2], [3] to enhance scaling mechanisms.
Taking into account both basic metrics such as CPU and memory usage, as well as higher-level metrics such as end-to-end latencies, these approaches strive to reflect the execution dynamics of microservice applica- tions
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Ente in cotutela
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
