Performance of Information Technology Infrastructure Prediction using Machine Learning
| Autor: | Yanto Setiawan, Novita Hanafiah, Ignatius Rahardjo Heruwidagdo, Suharjito |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer performance
Computer science business.industry Decision tree Information technology 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Random forest Server Information technology management 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Network performance Resource management Artificial intelligence business computer General Environmental Science |
| Zdroj: | Procedia Computer Science. 179:515-523 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.01.035 |
| Popis: | Resource management is always an important issue related to good governance decision making. One of the common problem faced in managing IT Infrastructure is about allocating server resources to improve the performance. In this study we use a machine learning approach to make predictions about the performance of information technology infrastructure. The experiment took data from several servers in a company to be tested. The performance measure of resources used in this study are CPU Performance, Disk performance, Memory capacity, and Network performance. Several algorithms and machine learning methods are tested, such as Linear Regression, kNN, SVR, Decision Tree and Random Forest, to find the best model fit for the servers. The comparison result shows that Linear regression and kNN perform well in predicting the network performance in those three servers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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