Microservices architecture has garnered extensive attention for its stability and scalability. However, in the complex and dynamic landscape of microservices systems, a incident in one service can propagate to others, resulting in significant economic losses and degraded user experiences. Therefore, the effective and precise localization of incidents in microservices systems becomes a critical concern. Previous research has leveraged runtime data (logs, metrics, call traces) and historical incident data to assist in root cause localization. However, due to the scarcity of business incidents (those causing severe impacts on business operations) and the fact that many incidents are reported by users, relevant run-time data and sufficient historical data are often unavailable, rendering previous methods impractical. In response to this challenge, we propose an online contrastive learning-based method for root cause localization of business incidents(OCRCL). We fully exploit incident tickets and the static dependency graph of services, integrating both textual semantic information and structural information from the dependency graph to discover root causes. Furthermore, we suggest that online contrastive learning can exhibit excellent performance with limited data and enable real-time model updates, making it better suited for industrial scenarios. Our approach demonstrates significant improvements over baseline methods across three real-world industrial datasets, highlighting its effectiveness in root cause localization.

In online service systems, a majority of incidents are caused by changes, which can influence user experience and cause huge economic loss. Experiences with a real-world, large-scale online service system show that more than half of the change-induced incidents are reported by users. Identifying root-cause changes for these incidents is challenging due to the inherent gap between user-perceived functional-level incident information and component-level change details. Inadequate causal knowledge also brings challenges. In this paper, we propose a novel causal knowledge mining based approach aiming at root-cause change identification for user-reported incidents named Raccoon. To bridge the gap between incidents and changes, it utilizes the fault tree and software product line to represent incidents and changes at the user-perceived functional level. They are also used as the backbone of causal knowledge. To overcome the lack of causal knowledge, Raccoon adopts efficient knowledge extraction and inference methods. Moreover, Raccoon provides recommendations at the software product line and change granularity to meet diverse demands of incident triage and root-cause change identification scenarios in incident management. We evaluate Raccoon on a real-world dataset collected in a large-scale online service system. The result shows that Raccoon significantly outperforms the state-of-the-art baseline approaches, which proves its effectiveness.

In online service systems, software changes cause a majority of incidents (i.e., unplanned interruptions and outages). Managing change-induced incidents efficiently is crucial for ensuring the reliability and availability of online service systems. Understanding the incidents can help improve change-induced incident management. The task is challenging because the life cycle of change-induced incidents is complicated due to diverse change deployment and incident resolution procedures. Detailed records of the incidents and changes, together with a comprehensive analysis, are needed to gain an in-depth understanding. In this paper, we conduct a qualitative and quantitative study on 231 change-induced incidents in a real-world, large-scale online service system. Detailed change tickets and incident timeline in the post-mortems provides extensive information about the incident life cycle, enabling us to understand each incident in depth. Based on the data, we give a generic model of the complicated life cycle of change-induced incidents. Following the model, we systematically study the whole life cycle of the incident, including the introduction and resolution stages, and answer what affects the efficiency of resolution. We obtain 9 major findings from our study. Based on the findings, we discuss existing techniques and promising future directions for improving change-induced incident management.

Monitoring forest biodiversity is essential to the conservation and management of forest resource. A new method called “spectranomics” that map forest species richness based on leaf biochemical and spectroscopic traits using imaging spectroscopy has been developed. In this study, we use this method combined with the airborne imaging spectroscopy (PHI-3 with 1m spatial resolution) data to detect the relationship among the spectral, biochemical and taxonomic diversity of tree species based on 20 dominant canopy species collected in the Longmenhe Forest Nature Reserve of China. Seven optimal biochemical components (chlorophyll, carotenoid, water, specific leaf area, nitrogen, cellulose, and lignin) are selected (R2>0.58, P4 points/m2). Finally, a self-adaptive Fuzzy C-Means (FCM) clustering algorithm is applied to determine the optimal clustering numbers (i.e. species richness) and Shannon-Wiener for each 30×30m window based on the isolated individual tree height and 7 biochemical indices. According to total 22 sample plots, the mapping results show that the predicted species richness is close to the field measurements (R2=0.65,P2=0.83, P<;0.01) than the species richness.