Signature-based IaaS Performance Change Detection

TL;DR

This paper introduces a new framework for detecting long-term performance changes in IaaS cloud services by analyzing performance signatures with advanced similarity measures, noise modeling, and SNR-based detection, validated on real datasets.

Contribution

It presents a novel change detection framework that combines performance signatures, a new noise model, and SNR-based detection for IaaS services.

Findings

Effective detection of performance changes demonstrated on real-world data

Distinguishes between noise and actual performance shifts

Improves long-term performance monitoring accuracy

Abstract

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.