Work

Signature-based IaaS Performance Change Detection

Year: 2024

Type: article

Abstract: We propose a novel change detection framework to identify changes in the long-term performance behavior of an Infrastructure as a Service (IaaS). An IaaS’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-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.

Source: ACM Transactions on Internet Technology

DOI: 10.1145/3702228

Authors: Sheik Mohammad Mostakim Fattah, Athman Bouguettaya

Institutions: The University of Sydney, Curtin University

Language: English

Topic: Software System Performance and Reliability

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Australian Research Council

Award: G1069494556