PMTT: Parallel multi-scale temporal convolution network and transformer for predicting the time to aging failure of software systems

Abstract

Software aging is one of the significant factors affecting the reliability and availability of long-running software systems, such as Android, Cloud systems, etc. The time to aging failure (TTAF) prediction for software systems plays a crucial role in proactive rejuvenation scheduling through machine learning or statistical analysis techniques, due to its ability to determine when to perform rejuvenation to mitigate the aging effects. However, software aging characterization is relatively complicated, and only fitting the variations for a single aging indicator cannot grasp the comprehensive degradation process across different case systems; moreover, since software systems often exhibit long and short-term inherent degradation characteristics, existing prediction models possess a poor ability for modeling both global and local information simultaneously. To tackle the above problems, a novel TTAF prediction framework based on the parallel multi-scale temporal convolution network and transformer (named PMTT) is proposed, by mapping various system running indicators reflecting the software aging to TTAF. PMTT possesses the following distinctive characteristics. First, a local feature extraction module that contains multiple channel TCNs with different scales is developed to extract inherent local information from the raw input. Second, in a parallel manner, a global feature extraction module integrating transformer blocks is built to extract global information representation synchronously using the self-attention mechanism. Afterward, high-level global–local features extracted from different channels are fused, and TTAF is estimated through two fully connected regression layers using the fused features. The proposed PMTT has been compared to seven competitors using run-to-failure data collected from Android and OpenStack systems. The experiments have demonstrated the superiority of PMTT, showing an average improvement of 11.2%, 9.0%, and 9.3% in performance across three evaluation metrics compared with the optimal baseline model.