Current studentsExisting staffContact us
 

Towards AI-Driven, Self-Adaptive, and Cyber-Resilient Software Engineering

Simple item page
dc.contributor.advisorSzabo, Claudia
dc.contributor.advisorTreude, Christoph (Singapore Management University)
dc.contributor.advisorWagner, Markus (Monash University)
dc.contributor.authorAhmad, Hussain
dc.contributor.schoolSchool of Computer and Mathematical Sciences
dc.date.issued2025
dc.description.abstractThe rapid advancement of technology, driven by digital transformation and Industry 4.0, has revolutionized software engineering, giving rise to next-generation architectures. At the heart of this revolution, microservice architectures represent a paradigm shift in software development, offering modularity, flexibility, and operational efficiency. These architectures seamlessly integrate with cloud computing, DevOps practices, and Continuous Integration/Continuous Deployment (CI/CD) pipelines, leveraging containerization to optimize software development, deployment and management. Recognizing these advantages, leading enterprises, such as Netflix and Amazon, have embraced microservice architectures to improve their quality of service. However, the widespread adoption of microservices naturally brings the challenge of managing highly dynamic and fluctuating workloads. To address this, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. Despite their intended benefits, existing HPAs face critical limitations in resource management during auto-scaling operations. These limitations stem from rigid microservice resource constraints, sluggish scaling actions, and disruptions caused by faults or cyber-attacks, leading to resource wastage, service unavailability, and degraded HPA performance. This PhD thesis contributes to advancing the body of knowledge on HPA-driven autoscaling in microservice architectures. Specifically, it aims to enhance the adaptability, proactivity, and resilience of HPA-based auto-scaling operations to optimize resource management, improve service availability, and enhance the robustness of microservice architectures. To achieve this objective, the thesis makes the following contributions: (1) Conduct a comprehensive literature review on HPA-driven auto-scaling in microservice architectures to examine HPA architectural designs and auto-scaling policies. This review reveals key resource management limitations that undermine the effectiveness of HPA auto-scaling in dynamic microservice environments. (2) Propose Smart HPA, a resource-efficient auto-scaler with an adaptive hierarchical architecture that integrates the advantages of centralized and decentralized Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control frameworks to overcome rigid microservice resource constraints, ensuring dynamic and efficient auto-scaling in microservice architectures. (3) Present ProSmart HPA, a proactive auto-scaler that extends the hierarchical MAPE-K control architecture to MAPE-KI by integrating an Artificial Intelligence (AI) component. This enhanced MAPE-KI architecture, combined with a proactive auto-scaling policy, mitigates delayed HPA responses, enabling timely and efficient auto-scaling in microservice environments. (4) Introduce SecureSmart HPA, a resilient auto-scaler featuring a three-layered adaptive hierarchical architecture that detects and analyzes microservice resource demands and disruptions, enabling disruption-aware scaling decisions in volatile and resource-constrained environments. Through these contributions, this thesis offers valuable insights for researchers and practitioners to advance both the state-of-the-art and state-of-the-practice in enhancing HPA-driven auto-scaling operations within microservice architectures.
dc.description.dissertationThesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 2025en
dc.identifier.urihttps://hdl.handle.net/2440/145509
dc.language.isoen
dc.provenanceThis electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legalsen
dc.subjectCloud Computing
dc.subjectArtificial Intelligence
dc.subjectAuto-scaling
dc.subjectMicroservices
dc.subjectResource Management
dc.subjectSoftware Architecture
dc.subjectKubernetes
dc.titleTowards AI-Driven, Self-Adaptive, and Cyber-Resilient Software Engineering
dc.typeThesisen

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Ahmad2025_PhD.pdf
Size:
2.45 MB
Format:
Adobe Portable Document Format
Download

Collections

Research Theses