Public defence in computer and information science: Mirko D’Angelo

Title: Engineering Decentralized Learning in Self-Adaptive Systems
Subject: Computer and information science
Faculty: Faculty of Technology
Date: Friday 28 May 2021 at 1.00 pm
Place: Room N1017, building N, Växjö. For the public: Via Zoom, https://lnu-se.zoom.us/j/62680700616?pwd=NmlPZWpTeUVBaktYbTllMWo5UDZQQT09
External reviewer: Professor Lionel Seinturier, University of Lille, France
Examining committee: Associate Professor Genaina Nunes Rodrigues, University of Brasilia, Brasilia
Associate Professor Patrizia Scandurra, University of Bergamo, Italy
Professor Tomáš Bureš, Charles University, Czech Republic
Chairperson: Professor Welf Löwe, Department of Computer Science and Media Technology, Linnaeus University
Main supervisor: Associate Professor Mauro Caporuscio, Department of Computer Science and Media Technology, Linnaeus University
Assistant supervisor: Senior Lecturer Jesper Andersson, Department of Computer Science and Media Technology, Linnaeus University
Examiner: Professor Danny Weyns, Department of Computer Science and Media Technology, Linnaeus University
Spikning: Friday 7 May 2021 at 11.30 am at the University library in Växjö, https://lnu-se.zoom.us/j/63956359888?pwd=ZFRSL0N4R1NUK1l2N2p3ZmhGOE04dz09

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