An Online Service Performance Prediction Learning Method

In order to improve the quality of service operations, it is necessary to take the initiative to prevent service failures and service performance fluctuations, instead of triggering handlers when service errors occur. Effective prediction and analysis of the large-scale services performance is an effective and feasible proactive prevention tool. However, the traditional service performance prediction model mostly adopts the full batch training mode, it is difficult to meet the real-time requirements of large-scale service calculation. Based on the comprehensive trade-off between the method of full batch learning and the stochastic gradient descent method, a large-scale service performance prediction model is established based on online learning, and a service performance prediction method is proposed based on small batch online learning. Through properly setting the batch parameters, the proposed approach only need to train the sample data with small batches in one iteration, the time efficiency is improved for large-scale service performance prediction.