HLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast

Abstract

Long-running software, such as cloud computing services, is now widely used in modern applications. As a result, the demand for high availability and performance has grown. However, these applications are more vulnerable to software aging issues and are more likely to fail due to the accumulation of mistakes in the system. One popular strategy for dealing with such aging-related problems is to plan prediction-based software rejuvenation activities based on previously obtained data from long-running software. Prediction algorithms enable the activation of a mitigation mechanism before the problem occurs. The long short-term memory (LSTM) neural network, the present state of the art in temporal series prediction, has demonstrated promising results when applied to software aging concerns. This study aims to anticipate software aging failures using a hybrid prediction model integrating long short-term memory models and statistical approaches. We emphasize the capabilities of each strategy in various long-running software scenarios and provide an untried hybrid model (hLSTM-aging) based on the union of Conv-LSTM networks and probabilistic methodologies, attempting to combine the strengths of both approaches for software aging forecasts. The hLSTM-aging prediction results revealed how hybrid models are a compelling solution for software-aging prediction. Experiments showed that hLSTM-aging increased MSE criteria by 8.54% to 50% and MAE criteria by 3.53% to 14.29% when compared to Conv-LSTM, boosting the model’s initial performance.