Open AccessModel-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques
Author(s) -
Haitham A.M Salih,
H.H. Ammar
Publication year - 2017
Publication title -
joiv : international journal on informatics visualization
Language(s) - English
Resource type - Journals
eISSN - 2549-9904
pISSN - 2549-9610
DOI - 10.30630/joiv.1.3.35
Subject(s) - computer science , workload , machine learning , unified modeling language , software , resource (disambiguation) , performance prediction , predictive modelling , software system , data mining , artificial intelligence , simulation , computer network , programming language , operating system
The growing complexity of modern software systems makes the performance prediction a challenging activity. Many drawbacks incurred by using the traditional performance prediction techniques such as time consuming and inability to surround all software system when large scaled. To contribute to solving these problems, we adopt a model-based approach for resource utilization and performance risk prediction. Firstly, we model the software system into annotated UML diagrams. Secondly, performance model is derived from UML diagrams in order to be evaluated. Thirdly, we generate performance and resource utilization training dataset by changing workload. Finally, when new instances are applied we can predict resource utilization and performance risk by using machine learning techniques. The approach will be used to enhance work of human experts and improve efficiency of software system performance prediction. In this paper, we illustrate the approach on a case study. A performance training dataset has been generated, and three machine learning techniques are applied to predict resource utilization and performance risk level. Our approach shows prediction accuracy within 68.9 % to 93.1 %.