SEAMS 2020
Mon 29 June - Fri 3 July 2020
co-located with ICSE 2020
Mon 29 Jun 2020 16:00 - 16:05 at SEAMS - Session 1: AI, Machine Learning and Statistics Chair(s): Pooyan Jamshidi

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r.t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation.

Mon 29 Jun
Times are displayed in time zone: (UTC) Coordinated Universal Time change

16:00 - 17:30: SEAMS 2020 - Session 1: AI, Machine Learning and Statistics at SEAMS
Chair(s): Pooyan JamshidiUniversity of South Carolina
seams-2020-papers16:00 - 16:05
Ricardo CaldasChalmers, Arthur RodriguesUniversity of Brası́lia, Eric Bernd GilUniversity of Brasilia, Genaína Nunes RodriguesUniversity of Brasília, Thomas VogelHumboldt-Universität zu Berlin, Patrizio PelliccioneUniversity of L'Aquila and Chalmers | University of Gothenburg
DOI Pre-print Media Attached
seams-2020-papers16:05 - 16:10
Jeroen Van Der DoncktKU Leuven, Danny WeynsKU Leuven, Federico QuinKatholieke Universiteit Leuven, Jonas Van Der DoncktGhent University, Sam MichielsKatholieke Universiteit Leuven
Pre-print Media Attached
seams-2020-papers16:10 - 16:15
Max ScheererFZI Research Center for Information Technology, Germany, Jonas KlamrothFZI Research Center for Information Technology, Germany, Ralf ReussnerKarlsruhe Institute of Technology (KIT) and FZI - Research Center for Information Technology (FZI), Bernhard BeckertKarlsruhe Institute of Technology
Media Attached
seams-2020-papers16:15 - 16:20
Yuning HeNASA Ames, Johann SchumannNASA Ames
Media Attached
seams-2020-papers16:20 - 17:20