XAI-Guided Optimization of a Multilayer Network Regression Model
Paper i proceeding, 2024
The recent technological advances create increased network capacity demand, highlighting the need for new network optimization methods. However, the proposed solutions require broad testing with numerous time-consuming simulations. Thus, estimation methods based on Machine Learning (ML) are developed to improve this process. In this work, we create a regression network model to predict four resource utilization metrics using the input set of connection requests. Using eXplainable Artificial Intelligence (XAI) tools, we optimize the proposed model for faster inference without a decrease in prediction quality.
multilayer network
explainable artificial intelligence
resource allocation
machine learning
Författare
Katarzyna Duszynska
Politechnika Wrocławska
Pawel Polski
Politechnika Wrocławska
Micha Wlosek
Politechnika Wrocławska
Aleksandra Knapinska
Politechnika Wrocławska
Piotr Lechowicz
Politechnika Wrocławska
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Krzysztof Walkowiak
Politechnika Wrocławska
2024 IFIP Networking Conference, IFIP Networking 2024
769-774
9783903176638 (ISBN)
23rd International Federation for Information Processing on Networking Conference, IFIP Networking 2024
Thessaloniki, Greece,
Thessaloniki, Greece,
Ämneskategorier
Produktionsteknik, arbetsvetenskap och ergonomi
Reglerteknik
DOI
10.23919/IFIPNetworking62109.2024.10619785