Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.
SummaryThis paper proposes a dynamic window with virtual goal (DW-VG) method for local collision avoidance in dynamic environments. Firstly, the debounce filter and polynomial curve-fitting algorithm are combined to predict the trajectory of the obstacles with timestamps. Based on the motion prediction of the obstacles, the virtual goal is proposed to replace the real goal, so that the robot can escape from the concave trap and avoid the dynamic obstacles. According to the timestamps and virtual goal, the optimal linear and angular velocities are selected from the dynamic window, which drive the robot toward its real goal. The simulation and experimental results show that the DW-VG method can not only escape the local minima and avoid dynamic obstacles but also is applicable to the dense environment. Furthermore, the simulation results also verify that the DW-VG method drives the robot to reach its goal faster and safer than other reactive obstacle avoidance methods.
Logs are one of the most critical data for service management. It contains rich runtime information for both services and users. Since size of logs are often enormous in size and have free handwritten constructions, a typical log-based analysis needs to parse logs into structured format first. However, we observe that most existing log parsing methods cannot parse logs online, which is essential for online services. In this paper, we present an automatic online log parsing method, name as LogStamp. We extensively evaluate LogStamp on five public datasets to demonstrate the effectiveness of our proposed method. The experiments show that our proposed method can achieve high accuracy with only a small portion of the training set. For example, it can achieve an average accuracy of 0.956 when using only 10% of the data training.
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