Using Causality for Enhanced Prediction of Web Traffic Time Series

TL;DR

This paper introduces CCMPlus, a neural network module that captures causal relationships between web services to significantly improve traffic prediction accuracy, validated on real-world datasets from major tech companies.

Contribution

The paper presents a novel causal relationship extraction module, CCMPlus, integrated with existing models to enhance web traffic time series prediction performance.

Findings

CCMPlus improves prediction accuracy over state-of-the-art methods.

Causal relationships among services are effectively captured by the proposed module.

Empirical validation on datasets from Microsoft Azure, Alibaba, and Ant Group confirms performance gains.

Abstract

Predicting web service traffic has significant social value, as it can be applied to various practical scenarios, including but not limited to dynamic resource scaling, load balancing, system anomaly detection, service-level agreement compliance, and fraud detection. Web service traffic is characterized by frequent and drastic fluctuations over time and are influenced by heterogeneous web user behaviors, making accurate prediction a challenging task. Previous research has extensively explored statistical approaches, and neural networks to mine features from preceding service traffic time series for prediction. However, these methods have largely overlooked the causal relationships between services. Drawing inspiration from causality in ecological systems, we empirically recognize the causal relationships between web services. To leverage these relationships for improved web service traffic prediction, we propose an effective neural network module, CCMPlus, designed to extract causal relationship features across services. This module can be seamlessly integrated with existing time series models to consistently enhance the performance of web service traffic predictions. We theoretically justify that the causal correlation matrix generated by the CCMPlus module captures causal relationships among services. Empirical results on real-world datasets from Microsoft Azure, Alibaba Group, and Ant Group confirm that our method surpasses state-of-the-art approaches in Mean Squared Error (MSE) and Mean Absolute Error (MAE) for predicting service traffic time series. These findings highlight the efficacy of leveraging causal relationships for improved predictions.