  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 of
services. It allows the monitoring team to prevent or diminish the impact of a
failure. Logs are a core part of software development and maintenance, by
recording detailed information at runtime. Such log data are universally
available in nearly all computer systems. They enable developers as well as
system maintainers to monitor and dissect anomalous events. For Cloud computing
companies and large online platforms in general, growth is linked to the
scaling potential. Automatizing the anomaly detection process is a promising
way to ensure the scalability of monitoring capacities regarding the increasing
volume of logs generated by modern systems. In this paper, we will introduce
MoniLog, a distributed approach to detect real-time anomalies within
large-scale environments. It aims to detect sequential and quantitative
anomalies within a multi-source log stream. MoniLog is designed to structure a
log stream and perform the monitoring of anomalous sequences. Its output
classifier learns from the administrator's actions to label and evaluate the
criticality level of anomalies.

Artificial Intelligence;Information Retrieval