Work

A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement

Year: 2024

Type: preprint

Abstract: Monitoring the status of large computing systems is essential to identify unexpected behavior and improve their performance and uptime. However, due to the large-scale and distributed design of such computing systems as well as a large number of monitoring parameters, automated monitoring methods should be applied. Such automatic monitoring methods should also have the ability to adapt themselves to the continuous changes in the computing system. In addition, they should be able to identify behavioral anomalies in useful time, to perform appropriate reactions. This work proposes a general lightweight and unsupervised method for near real-time anomaly detection using operational data measurement on large computing systems. The proposed model requires as little as 4 hours of data and 50 epochs for each training process to accurately resemble the behavioral pattern of computing systems.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2402.05114

Authors: Vargis, Tom Richard, Ghiasvand, Siavash

Institutions: Center for Scalable Data Analytics and Artificial Intelligence, Technische Universität Dresden

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Bundesministerium für Bildung und Forschung, Technische Universität Dresden

Award: G3652664658

OpenAlex ID: W4391709250

Cites:

Cited by:

Related to: 10