Online Reliability Prediction via Long Short Term Memory for Service-Oriented Systems

Abstract

A service-oriented System of System (SoS) integrates component services into a value-added and more complex system to satisfy the complex requirements of users. Due to a dynamic running environment, online reliability prediction for the loosely coupled component systems that ensures the runtime quality poses a major challenge and attracts growing attention. To guarantee the stable and continuous operation of systems, we propose a online reliability time series prediction method basing on long short term memory (LSTM), which is a modified Recurrent Neural Networks trained with historical reliability time series to predict the reliability of component systems in the near future. We conduct a series of experiments on a dataset composed of real web services and compare with other competitive approaches. Experimental results have demonstrated the effectiveness of our approach.