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ECSA 2021
Mon 13 - Fri 17 September 2021 Location to be announced

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.

Fri 17 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

17:00 - 18:00
Session 5: Machine learning for Software ArchitectureResearch Papers
Chair(s): Luciano Baresi Politecnico di Milano
17:00
20m
Paper
Explaining Architectural Tradeoff Spaces: a Machine Learning ApproachResearch Track
Research Papers
Javier Camara University of Málaga, Mariana Silva University of York, UK, David Garlan Carnegie Mellon University, Bradley Schmerl Carnegie Mellon University, USA
17:20
20m
Paper
A Machine Learning Approach to Service Discovery for Microservice ArchitecturesResearch Track
Research Papers
Mauro Caporuscio Linnaeus University, Marco De Toma University of L'Aquila, Henry Muccini University of L'Aquila, Italy, Karthik Vaidhyanathan University of L'Aquila
17:40
20m
Paper
A Reference Architecture for Federated Learning SystemsResearch Track
Research Papers
Sin Kit Lo CSIRO Data61, Qinghua Lu CSIRO Data61, Hye-Young Paik The University of New South Wales, Liming Zhu CSIRO’s Data61; UNSW