We propose a novel change detection framework to identify changes in the long-term performance behavior of an IaaS service. An IaaS service's long-term performance behavior is represented by an IaaS performance signature. The proposed framework leverages time series similarity measures and a sliding window technique to detect changes in IaaS performance signatures. We introduce a new IaaS performance noise model that enables the proposed framework to distinguish between performance noise and actual changes in performance. The proposed framework utilizes a novel Signal-to-Noise Ratio (SNR) based approach to detect changes when prior knowledge about performance noise is available. A set of experiments is conducted using real-world datasets to demonstrate the effectiveness of the proposed change detection framework.
Cite
@article{arxiv.2410.17623,
title = {Signature-based IaaS Performance Change Detection},
author = {Sheik Mohammad Mostakim Fattah and Athman Bouguettaya},
journal= {arXiv preprint arXiv:2410.17623},
year = {2025}
}
Comments
Published at ACM Transaction on Internet Technology. The paper was extended from the paper: arXiv:2007.11705