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.

TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

“…When the predicted aging indicator exceeds the predefined threshold, the time point is used as the basis for scheduling rejuvenation action. However, the aging indicator has complex variation characteristics, this gives rise to great difficulties in the prediction of software aging (Jia et al, 2023;Battisti et al, 2022). Nonetheless, this challenge has not stopped some peers from pursuing the aging prediction model with high accuracy.…”

“…Similar to Liu's work (Liu et al, 2019), Meng et al (2021) proposed the integration of ARIMA and recurrent neural network (RNN), to perform cloud server aging prediction, but their approach uses the predicted output of ARIMA and the original data as input features of RNN, and the experimental results are still promising compared to the single models. Lately, Battisti et al (2022) proposed a hybrid model (hLSTM-aging) based on the combination of Conv-LSTM networks and probabilistic method, which considered the strengths of both techniques in software aging prediction, and the performance of their approach is validated in a variety of aging indicators. Jia et al (2023) noted that seasonality is an important factor in formulating rejuvenation schedules, they combined time series decomposition with GRU to predict the memory resource consumption in cloud services, and verified the feasibility of the proposed method.…”