| Abstract |
With ever-increasing advancement of ICT (Information and Communications Technology) new challenges and requirements resulting from societal expectations emerge. Modern Cloud and Internet of Things feature functionalities that often involve not only data manipulation, but also mission critical tasks requiring ICT systems to strictly comply with defined nonfunctional requirements, especially in terms of web service execution time. These applications may range from solutions developed for e-Health, Industry 4.0, Smart Cities to applications related to fields like Smart Home or autonomous vehicles. Often, the QoS (Quality of Service) parameters are strongly correlated with user provided input and computational resources allocated to services that are handling the requests. To meet the needs of modern systems, new methods for QoS prediction are required, which can be later used by middleware responsible for resource allocation in service-based systems. This paper focuses on research related to QoS prediction methods. It discusses the general web service QoS parameters estimation problem and related challenges. Then a proposition of machine learning based approach for prediction of service QoS parameters based on the request data is presented, followed by experimentation aimed at evaluation of the proposed solution on real-word web services in a simulated environment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
