2026-08-17T12:52:28Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:7143 2024-03-06T11:04:59Z ddc:004 open_access

Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact) Schmid, Sanny Gerostathopoulos, Ilias Prehofer, Christian Bures, Tomas self-adaptation; Big Data analytics; model problem tool framework This artifact supports our research in self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. We are instead investigating a novel approach where the system model consists only of the essential input and output parameters and Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. In this artifact, we provide a concrete model problem that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool-based framework for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. The model problem (CrowdNav) and the framework (RTX) have been packaged together in this artifact, but can also work independently. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sanny Schmid and Ilias Gerostathopoulos and Christian Prehofer and Tomas Bures 2017 Is Part Of DARTS, Volume 3, Issue 1, Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DARTS.3.1.5 urn:nbn:de:0030-drops-71435 https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.5 eng https://creativecommons.org/licenses/by/3.0/de/legalcode