Service discovery mechanisms have continuously evolved during the last years to support the effective and efficient service composition in large-scale microservice applications. Still, the dynamic nature of services (and of their contexts) are being rarely taken into account for maximizing the desired quality of service. This paper proposes using machine learning techniques, as part of the service discovery process, to select microservice instances in a given context, maximize QoS, and take into account the continuous changes in the execution environment. Both deep neural networks and reinforcement learning techniques are used. Experimental results show how the proposed approach outperforms traditional service discovery mechanisms.

A Machine Learning Approach to Service Discovery for Microservice Architectures

De Toma M.;Muccini H.;
2021

Abstract

Service discovery mechanisms have continuously evolved during the last years to support the effective and efficient service composition in large-scale microservice applications. Still, the dynamic nature of services (and of their contexts) are being rarely taken into account for maximizing the desired quality of service. This paper proposes using machine learning techniques, as part of the service discovery process, to select microservice instances in a given context, maximize QoS, and take into account the continuous changes in the execution environment. Both deep neural networks and reinforcement learning techniques are used. Experimental results show how the proposed approach outperforms traditional service discovery mechanisms.
978-3-030-86043-1
978-3-030-86044-8
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/178645
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact