Although tremendous efforts have been devoted to the quality assurance of online service systems, in reality, these systems still come across many incidents (i.e., unplanned interruptions and outages), which can decrease user satisfaction or cause economic loss. To better understand the characteristics of incidents and improve the incident management process, we perform the first large-scale empirical analysis of incidents collected from 18 real-world online service systems in Microsoft. Surprisingly, we find that although a large number of incidents could occur over a short period of time, many of them actually do not matter, i.e., engineers will not fix them with a high priority after manually identifying their root cause. We call these incidents incidental incidents. Our qualitative and quantitative analyses show that incidental incidents are significant in terms of both number and cost. Therefore, it is important to prioritize incidents by identifying incidental incidents in advance to optimize incident management efforts. In particular, we propose an approach, called DeepIP (Deep learning based Incident Prioritization), to prioritizing incidents based on a large amount of historical incident data. More specifically, we design an attention-based Convolutional Neural Network (CNN) to learn a prediction model to identify incidental incidents. We then prioritize all incidents by ranking the predicted probabilities of incidents being incidental. We evaluate the performance of DeepIP using real-world incident data. The experimental results show that DeepIP effectively prioritizes incidents by identifying incidental incidents and significantly outperforms all the compared approaches. For example, the AUC of DeepIP achieves 0.808, while that of the best compared approach is only 0.624 on average.

In large-scale online service systems, incidents occur frequently due to a variety of causes, from updates of software and hardware to changes in operation environment. These incidents could significantly degrade system’s availability and customers’ satisfaction. Some incidents are linked because they are duplicate or inter-related. The linked incidents can greatly help on-call engineers find mitigation solutions and identify the root causes. In this work, we investigate the incidents and their links in a representative real-world incident management (IcM) system. Based on the identified indicators of linked incidents, we further propose LiDAR (Linked Incident identification with DAta-driven Representation), a deep learning based approach to incident linking. More specifically, we incorporate the textual description of incidents and structural information extracted from historical linked incidents to identify possible links among a large number of incidents. To show the effectiveness of our method, we apply our method to a real-world IcM system and find that our method outperforms other state-of-the-art methods.

In recent years, online service systems have become increasingly popular. Incidents of these systems could cause significant economic loss and customer dissatisfaction. Incident triage, which is the process of assigning a new incident to the responsible team, is vitally important for quick recovery of the affected service. Our industry experience shows that in practice, incident triage is not conducted only once in the beginning, but is a continuous process, in which engineers from different teams have to discuss intensively among themselves about an incident, and continuously refine the incident-triage result until the correct assignment is reached. In particular, our empirical study on 8 real online service systems shows that the percentage of incidents that were reassigned ranges from 5.43% to 68.26% and the number of discussion items before achieving the correct assignment is up to 11.32 on average. To improve the existing incident triage process, in this paper, we propose DeepCT, a Deep learning based approach to automated Continuous incident Triage. DeepCT incorporates a novel GRU-based (Gated Recurrent Unit) model with an attention-based mask strategy and a revised loss function, which can incrementally learn knowledge from discussions and update incident-triage results. Using DeepCT, the correct incident assignment can be achieved with fewer discussions. We conducted an extensive evaluation of DeepCT on 14 large-scale online service systems in Microsoft. The results show that DeepCT is able to achieve more accurate and efficient incident triage, e.g., the average accuracy identifying the responsible team precisely is 0.641~0.729 with the number of discussion items increasing from 1 to 5. Also, DeepCT statistically significantly outperforms the state-of-the-art bug triage approach.

Logs are widely used by large and complex software-intensive systems for troubleshooting. There have been a lot of studies on log-based anomaly detection. To detect the anomalies, the existing methods mainly construct a detection model using log event data extracted from historical logs. However, we find that the existing methods do not work well in practice. These methods have the close-world assumption, which assumes that the log data is stable over time and the set of distinct log events is known. However, our empirical study shows that in practice, log data often contains previously unseen log events or log sequences. The instability of log data comes from two sources: 1) the evolution of logging statements, and 2) the processing noise in log data. In this paper, we propose a new log-based anomaly detection approach, called LogRobust. LogRobust extracts semantic information of log events and represents them as semantic vectors. It then detects anomalies by utilizing an attention-based Bi-LSTM model, which has the ability to capture the contextual information in the log sequences and automatically learn the importance of different log events. In this way, LogRobust is able to identify and handle unstable log events and sequences. We have evaluated LogRobust using logs collected from the Hadoop system and an actual online service system of Microsoft. The experimental results show that the proposed approach can well address the problem of log instability and achieve accurate and robust results on real-world, ever-changing log data.