Abstract
Due to the complexity of the network structure, log analysis is usually necessary for the maintenance of network-based distributed systems since logs record rich information about the system behaviors. In recent years, numerous works have been proposed for log analysis; however, they ignore temporal relationships between logs. In this paper, we target on the problem of mining informative patterns from temporal log data. We propose an approach to discover sequential patterns from event sequences with temporal regularities. Discovered patterns are useful for engineers to understand the behaviors of a network-based distributed system. To solve the well-known problem of pattern explosion, we resort to the minimum description length (MDL) principle and take a step forward in summarizing the temporal relationships between adjacent events of a pattern. Experiments on real log datasets prove the efficiency and effectiveness of our method.
Highlights
With the increasing demand for computing power, many network-based distributed systems have emerged, such as the popular distributed storage system and HDFS
By informative, we mean a set of patterns that can summarize the log sequence well. e discovered patterns can help the operation engineers to have a better understanding of the system behaviors and serve as an excellent source of information for online monitoring and anomaly detection
We use four different log datasets collected from real systems. e basic statistics of these datasets are summarized in Table 2. e Zookeeper and OpenStack datasets are collected from the well-known loghub data repository [15], while the NameNode and DataNode logs are collected from our own HDFS cluster
Summary
With the increasing demand for computing power, many network-based distributed systems have emerged, such as the popular distributed storage system and HDFS. Distributed system utilizes multiple machine nodes to complete tasks based on the network. Since the network may be complex and each node in the network may report anomalies, to maintain the network-based distributed application, instead of monitoring each node of the network, experts usually analyze the node logs to evaluate the health of the system. Mining information from log sequences is a useful way to understand network-based distributed system behaviors. We target on the problem of mining informative patterns from temporal log sequence data. By informative, we mean a set of patterns that can summarize the log sequence well. E discovered patterns can help the operation engineers to have a better understanding of the system behaviors and serve as an excellent source of information for online monitoring and anomaly detection. Many informative patterns are generated during system operation, corresponding to different system behaviors
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