# hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast
## 1 Introduction  
As the prevalence of software programs in our everyday lives grows, the demand for availability and high performance has emerged as a critical problem in software development. As a result, many cloud computing systems now function for extended periods without failing or going offline. This long operating duration, however, can lead to system failures and deterioration of the program's internal state, i.e., software aging [1]. Memory leakage, failure to relinquish file handles or connections, fragmentation-related difficulties, the recurrence of failures, data corruption, and other factors can all contribute to software aging, according to [2]. Isolated software faults do not usually result in system failure, but they can accumulate and cause error propagation across the software system. Such proliferation can cause the system to fail catastrophically. As a result, there are several potential solutions for avoiding critical errors caused by the aging process of software. [PERSON] et al. [PERSON] et al. (2018) approach this problem as a prediction problem, monitoring certain selected applications and their usage of system resources over time. Using such information, one may foresee where the system will begin to exhibit software aging issues and subsequently execute rejuvenation to avert them. Some existing techniques in the state of the art for predicting aging-related failures, such as in our situation, include employing time series [PERSON] et al. (2018), neural networks [PERSON] et al. (2018), and long short-term memory (LSTM) networks [PERSON] et al. (2018). The earlier-mentioned problem of accumulating individual mistakes might be challenging to see empirically. However, such cumulative mistakes frequently leave unambiguous indications of system resource expenditure. A healthy program, for example, oscillates less in terms of speed and memory use. By monitoring a system's resource usage over time, it is possible to determine when indicators of aging arise and when mitigation and rejuvenation actions should start execution. Furthermore, because this data collections spreads over time, it is only logical to interpret them as time series, that is, changeable data points tracked across time. There are several algorithms commonly used for time series analysis that use statistical concepts, including the moving average (MA) [PERSON] (1996) and its variants, the auto-regressive integrated moving average (ARIMA) [PERSON] (1996) and the seasonal auto-regressive integrated moving average (SARIMA) [PERSON] (1996). Both models are suited to analyze time-series data or predict future points in the series. In addition, ARIMA models apply in some cases where the data show evidence of non-stationary behaviors. The model can use an initial differentiation step (corresponding to the \"integrated\" part of the model) one or more times to eliminate non-stationary characteristics. SARIMA works similarly to ARIMA but considers the seasonality parameter, thus making it more suitable for problems where the data have cyclic patterns. These algorithms, along with the auto regressive (AR) and the auto regressive moving average (ARMA) [PERSON] (1996) that follow the same pattern, are explored in this research. Recurrent neural networks, in addition to the statistical techniques outlined in the preceding paragraph, are cutting-edge for time-series analysis. Because of its capacity to discern patterns in sequences, this sort of network effectively forecasts new values in time series. LSTM models, on the other hand, are commonly utilized in this forecasting context at the cutting edge. Although LSTM models have shown promising results in fitting patterns of software aging [PERSON] et al. (2018), as time-series challenges grow with technology, new architectures built from the original LSTM might be employed to fit more complicated data and tackle other problems. When selecting the appropriate technique for predicting software aging issues, the range of hybrid models for LSTM might be deceptive.