# Event-based Detection of Changes in IaaS Performance Signatures

arXiv: https://arxiv.org/abs/2007.11705 · HTML: https://mlsanity.com/paper/2007.11705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Sequential Learning-based IaaS Composition](https://mlsanity.com/paper/2102.12598/) (2021)
- [Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2608.18555/) (2026)

## Abstract (arXiv)

We propose a novel ECA approach to manage changes in IaaS performance signatures. The proposed approach relies on the detection of anomalous performance behavior in the context of IaaS performance signatures. A novel anomaly-based event detection technique is proposed. It utilizes the experience of free trial users to detect potential changes in IaaS performance signatures. A signature change detection technique is proposed using the cumulative sum control chart analysis. Additionally, a self-adjustment method is introduced to improve the accuracy of the proposed approach. A set of experiments based on real-world datasets are conducted to show the effectiveness of the proposed approach.