An Intensity Estimation Application Based on Website Microservice Logs

The number of digital platforms that use cloud systems with microsystem architectures has increased day by day. By using public cloud systems efficiently, costs and expenses can be significantly reduced. This study tries to determine the necessary resource for the website by examining user activities for cloud resources management. A successful estimating system is essential for adjusting the price/performance balance of resource management. In this study, more than 1.5 million user logs with 18 different features were collected. SVM RBF and decision tree forest have been applied for this data. This study is shown that the SVM RBF method modeled the service rush time with an approximately 95% success rate. With the study, it has been revealed that a sound cloud resources management system can a significant economic benefit by adjusting the number of resources according to rush time prediction.