Since resource consumption is the main reason for software aging occurrences, many methods have been applied to accurately predict the resource consumption series. Among these methods, neural networks are powerfully applied to forecast the series data. For some existing problems of artificial neural networks such as the choice of initialization and local optimization, the improvements of neural networks are not only a hot research topic in the field of time series prediction but also a research hotspot in resource consumption prediction of software aging. In this paper, we propose a method for resource consumption prediction of software aging using deep belief nets (DBNs) with the restricted Boltzmann machine (RBM). This presented method contains the following steps. First, a pre-processing is introduced by two parts: smoothing data by a self-organizing map (SOM) and removing a linear trend by a difference method. Second, a method, DBN with two RBMs, is presented to capture the features and forecast future values. Third, a glowworm swarm optimization (GSO) method is used to learn the hyper-parameters of DBN with two RBMs. In the experiments, two types of resource consumption series are used to validate our proposed method compared with some state-of-the-art algorithms.
