As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use
of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The …

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web
systems rely on time series data to monitor and identify anomalies in real time, as well as to …

With the booming of online service systems, anomaly detection on multivariate time series,
such as a combination of CPU utilization, average response time, and requests per second …

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big
data generated by IT Operations processes, particularly in cloud infrastructures, to provide …

The management of cloud service incidents (unplanned interruptions or outages of a
service/product) greatly affects customer satisfaction and business revenue. After years of …

The development of cloud computing delivery models inspires the emergence of cloud-
native computing. Cloud-native computing, as the most influential development principle for …

System logs, which describe a variety of events of software systems, are becoming
increasingly popular for anomaly detection. However, for a large software system, current …

Timely anomaly detection of key performance indicators (KPIs), eg, service response time,
error rate, is of utmost importance to Web services. Over the years, many unsupervised deep …

Logs are imperative in the management process of networks and services. However,
manually identifying and classifying anomalous logs is time-consuming, error-prone, and …