Towards Performance Analysis for Online Data Science Learning Platform

The online data science learning platform offers students an adaptable educational experience and a versatile learning and experimental environment. However, with the escalation of system concurrency, performance concerns including resource limitations, protracted online experiment response, and platform breakdowns, all of which can have a detrimental effect on the advancement of data science experimental learning. This paper proposes a performance analysis system for online data science learning platforms. It uses the Application Performance Index (Apdex) to evaluate user satisfaction of system performance, and promptly detects real-time performance anomalies and forecasts performance trends by LightGBM and LSTM algorithm. Meanwhile, the performance bottlenecks are also identified and correlated based on the function invocation chains with various time series performance metrics data. Therefore, negative user experience is effectively prevented. Furthermore, we conducted a series of experiments based on 40 days of operational data from a real online data science learning platform, the effectiveness and superiority of the proposed method in this system are clearly verified.