Performance Prediction Based on Random Forest for the Stream Processing Checkpoint

作者：Zheng Chu, Jiong Yu · 年份：2020 · DOI：10.11999/jeit190552 · 被引用次数：3 · 研究领域：Data Stream Mining Techniques、Anomaly Detection Techniques and Applications、Artificial Intelligence in Healthcare

Since real-time processing scenarios for ever-increasing amount and type of streaming data caused by the development of the Internet of Things (IoT) keep increasing, and strategies based on empirical knowledge for checkpoint configuration are deficiencies, the strategy faces huge challenges, such as time-consuming, labor-intensive, causing system anomalies, etc. To address these challenges, regression algorithm-based prediction is proposed for checkpoint performance. Firstly, six kinds of features, which have a huge influence on the performance, are analyzed, and then feature vectors of the training set are input into the regression algorithms for training, finally, test sets are used for the checkpoint performance prediction. Compared with other machine learning algorithms, the experimental results illustrat that the Random Forest (RF) has lower errors, higher accuracy and faster execution on CPU intensive benchmark, memory intensive benchmark and network intensive benchmark.