EXEHDA-RR
Renato Dilli, Huberto Kaiser Filho, Ana Marilza Pernas, Adenauer Yamin · 2017
Currently, a lot of resources are connected to the Internet, many simultaneously requesting and providing services. The adequate selection of resources that best meet the demands of users with a broad range of options has been a relevant and current research challenge. Based on the non-functional parameters of QoS play a significant role in the ranking of these resources according to the services they offer. This paper aims to aggregate machine learning in the pre-classification of EXEHDA middleware resources, to reduce the computational cost generated by MCDA algorithms. We presented the proposed software architecture (EXEHDA-RR), and the obtained results with the integration of machine learning in the classification process are promissing, and indicate to the research continuation.