Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Cloud applications are increasingly shifting from large monolithic services to complex
graphs of loosely-coupled microservices. Despite the advantages of modularity and …

Software architecture is undergoing a transition from monolithic architectures to
microservices to achieve resilience, agility and scalability in software development …

Modern society is increasingly moving toward complex and distributed computing systems.
The increase in scale and complexity of these systems challenges O&M teams that perform …

Driven by the emerging business models (eg, digital sales) and IT technologies (eg, DevOps
and Cloud computing), the architecture of software is shifting from monolithic to microservice …

Anomaly detection in multivariate time series data is of paramount importance for ensuring
the efficient operation of large-scale systems across diverse domains. However, accurately …

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are
impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI …

Unsupervised outlier detection is a vital task and has high impact on a wide variety of
applications domains, such as image analysis and video surveillance. It also gains long …

Effectively detecting run-time performance anomalies is crucial for clouds to identify
abnormal performance behavior and forestall future incidents. To be used for real-world …

With the growing complexity and scale of high performance computing (HPC) systems,
application performance variation has become a significant challenge in efficient and …