avhandlingar

Disputation i data- och informationsvetenskap: Mirko D’Angelo

Titel:  Engineering decentralized learning in self-adaptive systems
Ämne: Data- och informationsvetenskap
Fakultet: Fakulteten för teknik
Datum: Fredagen den 28 maj 2021 kl 13.00
Plats: Sal N1017, hus N, Växjö. För publik: Via Zoom (länken läggs ut två timmar i förväg).
Opponent: Professor Lionel Seinturier, Université de Lille, Frankrike
Betygsnämnd: Associate professor Genaina Nunes Rodrigues, University of Brasilia, Brasilien
Associate professor Patrizia Scandurra, University of Bergamo, Italien
Professor Tomáš Bureš, Charles University, Tjeckien
Ordförande: Professor Welf Löwe, Institutionen för datavetenskap och medieteknik, Linnéuniversitetet
Huvudhandledare: Docent Mauro Caporuscio, Institutionen för datavetenskap och medieteknik, Linnéuniversitetet
Biträdande handledare: Lektor Jesper Andersson, Institutionen för datavetenskap och medieteknik, Linnéuniversitetet
Examinator: Professor Danny Weyns, Institutionen för datavetenskap och medieteknik, Linnéuniversitetet
Spikning: Fredagen den 7 maj 2021 kl 11.30 på universitetsbiblioteket i Växjö

Abstract

Future computing environments are envisioned to be populated by myriads of pervasive real-world things, which collaborate to offer boundless opportunities to industry and society – e.g., smart cities, and intelligent transportation systems. In this setting, an application can be considered as a network-based system where applications dynamically emerge as opportunistic assemblies of services. This class of applications is likely characterized by high dynamism, with services joining and leaving the network and changing their quality attributes. Indeed, dynamic introduces uncertainty, which in turn may alter the system’s functionalities and harm the system’s quality of service.

Although self-adaptation and machine learning techniques are proposed as viable approaches to address run-time uncertainties and support resilience, engineering effectively these systems is undoubtedly complex, since their peculiarities demand decentralized solutions.

To this end, this thesis addresses the critical challenges for engineering decentralized learning in self-adaptive systems in three steps. First, it examines, classifies, and distills knowledge from research related to self-adaptive systems using learning techniques as means of addressing uncertainty. Then, it presents a reasoning framework that supports architecting and implementation activities with capabilities to evaluate architectural decisions. Finally, leveraging the solutions devised by addressing the aforementioned challenges, it proposes an approach to build and maintain over time a resilient assembly of services that are collectively able to deliver quality of service.

Evaluation is performed through an extensive set of simulation experiments to assess the effectiveness of the approach. The results show that the devised solution, including self-adaptation and reinforcement learning as key elements, can cope with unpredictably variable operating environments and guarantee quality of service and resilience.

Keywords: Self-adaptive systems, service assembly, resiliency, decentralized
control architecture, machine learning, reasoning framework

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