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Within today's large-scale systems, one anomaly can impact millions of users.Detecting such events in real-time is essential to maintain the quality ofservices. It allows the monitoring team to prevent or diminish the impact of afailure. Logs are a core part of software development and maintenance, byrecording detailed information at runtime. Such log data are universallyavailable in nearly all computer systems. They enable developers as well assystem maintainers to monitor and dissect anomalous events. For Cloud computingcompanies and large online platforms in general, growth is linked to thescaling potential. Automatizing the anomaly detection process is a promisingway to ensure the scalability of monitoring capacities regarding the increasingvolume of logs generated by modern systems. In this paper, we will introduceMoniLog, a distributed approach to detect real-time anomalies withinlarge-scale environments. It aims to detect sequential and quantitativeanomalies within a multi-source log stream. MoniLog is designed to structure alog stream and perform the monitoring of anomalous sequences. Its outputclassifier learns from the administrator's actions to label and evaluate thecriticality level of anomalies.

MoniLog: An Automated Log-Based Anomaly Detection System for Cloud Computing Infrastructures