arXiv subjects

Amjad Ullah

Publications and source records attributed to Amjad Ullah.

6 recordsLinked to original sources

arXiv2026

NL-CPS: Reinforcement Learning-Based Kubernetes Control Plane Placement in Multi-Region Clusters

The placement of Kubernetes control-plane nodes is critical to ensuring cluster reliability, scalability, and performance, and therefore represents a significant deployment challenge in heterogeneous, multi-region environments. Existing initialisation procedures typically select control-plane hosts arbitrarily, without considering node resource capacity or network topology, often leading to suboptimal cluster performance and reduced resilience. Given Kubernetes's status as the de facto standard for container orchestration, there is a need to rigorously evaluate how control-plane node placement influences the overall performance of the cluster operating across multiple regions. This paper advances this goal by introducing an intelligent methodology for selecting control-plane node placement across dynamically selected Cloud-Edge resources spanning multiple regions, as part of an automated orchestration system. More specifically, we propose a reinforcement learning framework based on neural contextual bandits that observes operational performance and learns optimal control-plane placement policies from infrastructure characteristics. Experimental evaluation across several geographically distributed regions and multiple cluster configurations demonstrates substantial performance improvements over several baseline approaches.

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arXiv2025

Distributed Resource Selection for Self-Organising Cloud-Edge Systems

This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distributed, and self-organising orchestration system that facilitates the intelligent placement and adaptation of applications in real-time. This work leverages a consensus-based mechanism utilising local knowledge and inter-agent collaboration to achieve efficient results without relying on a central controller, thus paving the way for distributed orchestration. Our results indicate that computation time is the key factor influencing allocation decisions. Our approach consistently delivers rapid allocations without compromising optimality or incurring additional cost, achieving timely results at scale where exhaustive search is infeasible and centralised heuristics run up to 30 times slower.

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arXiv2025

Kubernetes in the Cloud vs. Bare Metal: A Comparative Study of Network Costs

Modern cloud-native applications increasingly utilise managed cloud services and containerisation technologies, such as Kubernetes, to achieve rapid time-to-market and scalable deployments. Organisations must consider various factors, including cost implications when deciding on a hosting platform for containerised applications as the usage grows. An emerging discipline called FinOps combines financial management and cloud operations to optimise costs in cloud-based applications. While prior research has explored system-level optimisation strategies for cost and resource efficiency in containerized systems, analysing network costs in Kubernetes clusters remains underexplored. This paper investigates the network usage and cost implications of containerised applications running on Kubernetes clusters. Using a methodology that combines measurement analysis, experimentation, and cost modelling, we aim to provide organisations with actionable insights into network cost optimisation. Our findings highlight key considerations for analysing network expenditures and evaluating the potential cost benefits of deploying applications on cloud providers. Overall, this paper contributes to the emerging FinOps discipline by addressing the financial and operational aspects of managing network costs in cloud-native environments.

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arXiv2025

Optimising Intrusion Detection Systems in Cloud-Edge Continuum with Knowledge Distillation for Privacy-Preserving and Efficient Communication

The growth of the Internet of Things has amplified the need for secure data interactions in cloud-edge ecosystems, where sensitive information is constantly processed across various system layers. Intrusion detection systems are commonly used to protect such environments from malicious attacks. Recently, Federated Learning has emerged as an effective solution for implementing intrusion detection systems, owing to its decentralised architecture that avoids sharing raw data with a central server, thereby enhancing data privacy. Despite its benefits, Federated Learning faces criticism for high communication overhead from frequent model updates, especially in large-scale Cloud-Edge infrastructures. This paper explores Knowledge Distillation to reduce communication overhead in Cloud-Edge intrusion detection while preserving accuracy and data privacy. Experiments show significant improvements over state-of-the-art methods.

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arXiv2025

Towards a Decentralised Application-Centric Orchestration Framework in the Cloud-Edge Continuum

The efficient management of complex distributed applications in the Cloud-Edge continuum, including their deployment on heterogeneous computing resources and run-time operations, presents significant challenges. Resource management solutions -- also called orchestrators -- play a pivotal role by automating and managing tasks such as resource discovery, optimisation, application deployment, and lifecycle management, whilst ensuring the desired system performance. This paper introduces Swarmchestrate, a decentralised, application-centric orchestration framework inspired by the self-organising principles of Swarms. Swarmchestrate addresses the end-to-end management of distributed applications, from submission to optimal resource allocation across cloud and edge providers, as well as dynamic reconfiguration. Our initial findings include the implementation of the application deployment phase within a Cloud-Edge simulation environment, demonstrating the potential of Swarmchestrate. The results offer valuable insight into the coordination of resource offerings between various providers and optimised resource allocation, providing a foundation for designing scalable and efficient infrastructures.

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arXiv2023

Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems has been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, and heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.

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