Software Aging Prediction for Cloud Services Using a Gate Recurrent Unit Neural Network Model Based on Time Series Decomposition

Software aging, which is caused by the accumulation of errors in the system and the consumption of computing resources, tends to occur in long-running cloud service software systems. In practice, software aging prediction has proven to be useful in planning the time to trigger rejuvenation because it provides a prior estimate of future resource consumption. However, aging indicators (e.g., physical memory) in cloud may have the characteristics of long-term slow growth, medium-term seasonality variations (alternating peaks and troughs), and short-term irregular fluctuations. Unfortunately, most of the existing aging prediction methods (e.g., a statistical or single machine learning model) only focus on the accuracy of short-term prediction, while lacking the cognition of the medium and long-term variations of aging indicators and their functions in formulating the rejuvenation schedule (e.g., performing rejuvenation when the load is low can minimize interference to users). To address the above problems, this article proposes a novel hybrid aging prediction framework to work on the prediction of memory resource consumption in cloud by applying a seasonal-trend decomposition procedure based on loess (STL) method and Gate Recurrent Unit (GRU) neural network, called the decomposition-based GRU (DGRU). The effectiveness of DGRU mainly has two aspects. One is the STL method as a preprocessing technology that can extract the trend, seasonality, and residual characteristics from memory utilization data. The other is that these characteristics can be well predicted separately by a well-designed GRU model, which can model the time-series relationship between the data. Experimental results show that our DGRU framework has superior performance compared with its competitors, including seven single models and six hybrid models. Our study illustrates that the DGRU is a promising solution for high-precision software aging prediction.