SiaSL: A Siamese Neural Network for Service Level Prediction

Service level is an important metric to measure the reasonability of service systems. However, traditional mathematical service level prediction models have strict restrictions and suffer from accuracy degradation in complex real scenarios. In this paper, we propose to use a Siamese neural network to solve the service level prediction problem. This task is challenging due to two reasons: insufficient information and prior knowledge constraint. To tackle these issues, we develop a method entitled SiaSL based on the Siamese neural network. Our model consists of three key modules: 1) a time embedding module to embed the time period into low-dimensional vectors to model the time-independent characteristics of the service level; 2) a feature extractor to extract deep feature from raw scheduling information; 3) an output module to predict the final service level. Besides, to make SiaSL learn the prior knowledge, we propose a data augmentation method and an iterative training mechanism. Extensive experiments on a real-world dataset validate the effectiveness and efficiency of our method. On the premise of satisfying prior knowledge, our model achieves state-of-the-art performance on this problem.