Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques
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 solvi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Padang
2017-07-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | http://joiv.org/index.php/joiv/article/view/35 |
| _version_ | 1818138299122843648 |
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| author | Haitham A.M Salih Hany H Ammar |
| author_facet | Haitham A.M Salih Hany H Ammar |
| author_sort | Haitham A.M Salih |
| collection | DOAJ |
| description | 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 %. |
| first_indexed | 2024-12-11T10:09:59Z |
| format | Article |
| id | doaj.art-46e6726ab5af497e8af5a416dd213b22 |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-12-11T10:09:59Z |
| publishDate | 2017-07-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-46e6726ab5af497e8af5a416dd213b222022-12-22T01:11:46ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042017-07-011310110910.30630/joiv.1.3.3520Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning TechniquesHaitham A.M Salih0Hany H Ammar1College of Graduate Studies, Sudan University of Science and Technology, Khartoum, SudanLane Department of Computer Science and Electrical Engineering, West Virginia University, West Virginia, USAThe 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 %.http://joiv.org/index.php/joiv/article/view/35machine learningperformancerisk predictionWEKA. |
| spellingShingle | Haitham A.M Salih Hany H Ammar Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques JOIV: International Journal on Informatics Visualization machine learning performance risk prediction WEKA. |
| title | Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques |
| title_full | Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques |
| title_fullStr | Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques |
| title_full_unstemmed | Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques |
| title_short | Model-Based Resource Utilization and Performance Risk Prediction using Machine Learning Techniques |
| title_sort | model based resource utilization and performance risk prediction using machine learning techniques |
| topic | machine learning performance risk prediction WEKA. |
| url | http://joiv.org/index.php/joiv/article/view/35 |
| work_keys_str_mv | AT haithamamsalih modelbasedresourceutilizationandperformanceriskpredictionusingmachinelearningtechniques AT hanyhammar modelbasedresourceutilizationandperformanceriskpredictionusingmachinelearningtechniques |