infinite-dataset-hub/ServiceMonitor

ServiceMonitor数据集包含从各种基于Linux的系统中提取的系统日志条目，这些日志已被手动标注以反映系统上不同服务的健康状态。标注包括正常操作、高延迟、CPU过度使用、内存泄漏、磁盘故障和未经授权的访问等类别。这些标注对于训练机器学习模型以检测服务健康中的异常至关重要。数据集的目标是帮助开发能够自动化服务健康监控和异常检测的模型。

The ServiceMonitor dataset comprises of system log entries extracted from various Linux-based systems. These logs have been manually annotated to reflect the health status of different services running on these systems. The dataset includes labels that classify the logs into categories like Normal Operation, High Latency, CPU Overutilization, Memory Leak, Disk Failure, and Unauthorized Access. These labels are essential for machine learning models to train and detect anomalies in service health. The aim of this dataset is to assist in developing models that can automate service health monitoring and anomaly detection on Linux systems.