Online Reliability Prediction for Web Applications: An Adaptive Approach with AdaRel
Chun Yen Chang-Sundin, Ninad Chaudhari, Mei-Hwa Chen · 2024
Web applications provide essential and ubiquitous services across diverse application domains. Online reliability prediction forecasts the probability of a request being successfully processed within a given timeframe, providing valuable information for users and engineers. Many approaches have been proposed for software reliability modeling. However, conventional methods tend to rely heavily on historical data for assessing software reliability, often overlooking the dynamic nature of web applications and their implications for reliability predictions.This paper introduces AdaRel, an online reliability prediction system designed to adapt to the dynamic behavior of web applications, thereby enhancing prediction accuracy. AdaRel employs a suite of prediction models and periodically evaluates and selects the best-performing model to forecast reliability for each observation period. Additionally, it monitors the system’s behavior and strategically mitigates the adverse impacts on prediction accuracy when detecting an anomaly.In three case studies, AdaRel’s predictive accuracy consistently surpassed that of the individual algorithms. The results confirm that AdaRel’s performance is robust and dependable, irrespective of the distinctive attributes of the web applications it assesses.