SARO: A Secure Adaptive Resource Orchestrator for Intelligent Cloud Management Using Machine Learning and Reinforcement Learning

Alibrahem, U; Nanda, P; Dinh, H

SARO: A Secure Adaptive Resource Orchestrator for Intelligent Cloud Management Using Machine Learning and Reinforcement Learning

Alibrahem, U Nanda, P

Dinh, H

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2026 20th International Conference on Ubiquitous Information Management and Communication Imcom 2026, 2026, 00, pp. 1-8
Issue Date:: 2026-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted versionAdobe PDF (1.25 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Alibrahem, U
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X
dc.contributor.author	Dinh, H
dc.date	2026-01-04
dc.date.accessioned	2026-05-27T02:25:04Z
dc.date.available	2026-05-27T02:25:04Z
dc.date.issued	2026-01-01
dc.identifier.citation	Proceedings of the 2026 20th International Conference on Ubiquitous Information Management and Communication Imcom 2026, 2026, 00, pp. 1-8
dc.identifier.uri	http://hdl.handle.net/10453/195158
dc.description.abstract	Cloud platforms face challenges in dynamically managing resources while ensuring security for hosted applications. Reactive resource allocation often leads to latency spikes or resource wastage, and cyber-attacks or anomalies can further degrade performance. This paper presents Secure Adaptive Resource Orchestrator (SARO), a framework that integrates machine learning (ML) prediction, anomaly detection, and reinforcement learning (RL) for proactive cloud resource orchestration. SARO's five-layer architecture combines an LSTM-based workload predictor, an autoencoderbased anomaly detector, and a Q-learning orchestrator, and can be extended to advanced RL methods such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and SARSA for complex decision spaces. We implemented SARO using opensource tools including OpenStack, Kubernetes, TensorFlow, and PyTorch, and evaluated it using Google cluster workloads and the UNSW-NB15 security dataset. Enhanced with statistical analysis and semi-intelligent Kubernetes Horizontal Pod Autoscaler (HPA) baseline, results from ten independent trials show that SARO achieves 78 pm 4.2 % CPU utilization and 130 pm 8 ms latency, outperforming the Dynamic baseline (70 pm 3.9 %, 150 pm 20 ms) and HPA (68 pm 4.4 %, 155 pm 25 ms)(p< 0.05). SARO maintains 95% anomaly-detection accuracy with 5% false positives and zero SLA violations, confirming statistically significant improvements in efficiency, responsiveness, and security compared to conventional approaches.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	Proceedings of the 2026 20th International Conference on Ubiquitous Information Management and Communication Imcom 2026
dc.relation.ispartof	2026 20th International Conference on Ubiquitous Information Management and Communication (IMCOM)
dc.relation.isbasedon	10.1109/IMCOM69009.2026.11360802
dc.rights	info:eu-repo/semantics/openAccess
dc.title	SARO: A Secure Adaptive Resource Orchestrator for Intelligent Cloud Management Using Machine Learning and Reinforcement Learning
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Cyber Digital Centre (CDC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2026-05-27T02:25:03Z
pubs.finish-date	2026-01-06
pubs.publication-status	Published
pubs.start-date	2026-01-04
pubs.volume	00

Abstract:

Cloud platforms face challenges in dynamically managing resources while ensuring security for hosted applications. Reactive resource allocation often leads to latency spikes or resource wastage, and cyber-attacks or anomalies can further degrade performance. This paper presents Secure Adaptive Resource Orchestrator (SARO), a framework that integrates machine learning (ML) prediction, anomaly detection, and reinforcement learning (RL) for proactive cloud resource orchestration. SARO's five-layer architecture combines an LSTM-based workload predictor, an autoencoderbased anomaly detector, and a Q-learning orchestrator, and can be extended to advanced RL methods such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and SARSA for complex decision spaces. We implemented SARO using opensource tools including OpenStack, Kubernetes, TensorFlow, and PyTorch, and evaluated it using Google cluster workloads and the UNSW-NB15 security dataset. Enhanced with statistical analysis and semi-intelligent Kubernetes Horizontal Pod Autoscaler (HPA) baseline, results from ten independent trials show that SARO achieves 78 pm 4.2 % CPU utilization and 130 pm 8 ms latency, outperforming the Dynamic baseline (70 pm 3.9 %, 150 pm 20 ms) and HPA (68 pm 4.4 %, 155 pm 25 ms)(p< 0.05). SARO maintains 95% anomaly-detection accuracy with 5% false positives and zero SLA violations, confirming statistically significant improvements in efficiency, responsiveness, and security compared to conventional approaches.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/195158