Abstract

High-quality multivariate time-series datasets are significantly less accessible compared to more common data types such as images or text, due to the resource-intensive process of continuous monitoring, precise annotation, and long-term observation. This paper introduces a cost- effective solution in the form of a large-scale, curated dataset specifically designed for anomaly detection in computing systems’ performance metrics. The dataset encompasses 45 GB of multivariate time-series data collected from 66 systems, capturing key performance indicators such as CPU usage, memory consumption, disk I/O, system load, and power consumption across diverse hardware configurations and real-world usage scenarios. Annotated anomalies, including performance degradation and resource in efficiencies, provide a reliable benchmark and ground truth for evaluating anomaly detection models. By addressing the accessibility challenges associated with time- series data, this resource facilitates advancements in ma chine learning applications, including anomaly detection, predictive maintenance, and system optimization. Its comprehensive and practical design makes it a foundational asset for researchers and practitioners dedicated to developing reliable and efficient computing systems.

Authors

Veena More, Sowmya Kella, K. Ramesh
Karnataka State Akkamahadevi Women’s University, India

Keywords

Multivariate, Time Series, Laptop Performance