Error logs like Java exceptions play a crucial role in diagnosing and resolving errors within the industry. Nonetheless, the extensive logging of Java exceptions may result in exception fatigue in large-scale Java systems at an industrial level, where the frequency of Java exceptions being generated surpasses developers’ ability to manage them effectively. Regrettably, there is a lack of research on the seriousness, prevalence, and solutions to this problem. To close this gap, we first make a comprehensive investigation into the exception fatigue problem within a prominent Internet corporation in China, namely Alibaba, confirming its importance in the industry. Consequently, we introduce a novel solution called ABEL , designed to automatically pinpoint the most relevant exceptions associated with software failures. The key challenge lies in the randomness of exceptions, which prevents existing sequence-based techniques from being effective. To address this challenge, ABEL establishes correlations between Java exceptions and the Key Performance Indicator (KPI) of applications, enabling the identification of exceptions leading to irregularities in KPI. Our evaluation of ABEL across four Java applications and five business KPIs within Alibaba illustrates its capability to pinpoint the primary cause of exception logs with an AC@5 (top-5 accuracy) exceeding 90%, effectively mitigating the exception fatigue problem within Alibaba. Furthermore, it can identify the root-cause exceptions in a real software failure within just 4 minutes, outperforming the manual investigation process by over an hour.

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as “alert storm”. In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert$a$would lead to the occurrence of alert$b$. Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

Microwave remote sensing technology has played an important role in global ocean observation, environmental early warning and maritime security. In order to meet the different needs of ocean observation, satellites often need to carry multiple remote sensors for detection, which causes a serious burden on satellites. Therefore, the parameter design method of multi-mode small satellite synthetic aperture radar (SAR) system is studied. A single remote sensor in this system can realize four working modes of SAR, Altimeter, Spectrometer and Scatterometer in time sharing. Since a single remote sensor implements four modes of operation in time sharing, it is necessary to fully consider the constraints that may be encountered when using the same hardware system to implement the four modes of operation. According to the parameter design method of multi-mode small satellite SAR system, the parameters of radar system in four working modes can be designed.