arXiv subjects

Paul Harvey

Publications and source records attributed to Paul Harvey.

At least 19 recordsLinked to original sources

arXiv2026

From Subsumption to Satisfiability: LLM-Assisted Active Learning for OWL Ontologies

In active learning, membership queries (MQs) allow a learner to pose questions to a teacher, such as ''Is every apple a fruit?'', to which the teacher responds correctly with yes or no. These MQs can be viewed as subsumption tests with respect to the target ontology. Inspired by the standard reduction of subsumption to satisfiability in description logics, we reformulate each candidate axiom into its corresponding counter-concept and verbalise it in controlled natural language before presenting it to Large Language Models (LLMs). We introduce LLMs as a third component that provides real-world examples approximating an instance of the counter-concept. This design property ensures that only Type II errors may occur in ontology modelling; in the worst case, these errors merely delay the construction process without introducing inconsistencies. Experimental results on 13 commercial LLMs show that recall, corresponding to Type II errors in our framework, remains stable across several well-established ontologies.

cs.AI↗

arXiv2026

Code vs Serialized AST Inputs for LLM-Based Code Summarization: An Empirical Study

Summarizing source code into natural language descriptions (code summarization) helps developers better understand program functionality and reduce the burden of software maintenance. Abstract Syntax Trees (ASTs), as opposed to source code, have been shown to improve summarization quality in traditional encoder-decoder-based code summarization models. However, most large language model (LLM)-based code summarization methods rely on raw code or only incorporate partial AST signals, meaning that the potential of complete AST representation has not been fully explored for LLMs. This paper presents AST(NIT), an AST augmentation and serialization method that preserves lexical details and encodes structural information into LLM-compatible sequences. Experiments with the LLaMA-3.1-8B model on the CodeXGLUE Python dataset show that the proposed serialized ASTs reduce the length of LLM inputs, require shorter training times, and achieve summarization quality comparable to existing approaches.

cs.SE↗

arXiv2025

Simultaneous Genetic Evolution of Neural Networks for Optimal SFC Embedding

The reliance of organisations on computer networks is enabled by network programmability, which is typically achieved through Service Function Chaining. These chains virtualise network functions, link them, and programmatically embed them on networking infrastructure. Optimal embedding of Service Function Chains is an NP-hard problem, with three sub-problems, chain composition, virtual network function embedding, and link embedding, that have to be optimised simultaneously, rather than sequentially, for optimal results. Genetic Algorithms have been employed for this, but existing approaches either do not optimise all three sub-problems or do not optimise all three sub-problems simultaneously. We propose a Genetic Algorithm-based approach called GENESIS, which evolves three sine-function-activated Neural Networks, and funnels their output to a Gaussian distribution and an A* algorithm to optimise all three sub-problems simultaneously. We evaluate GENESIS on an emulator across 48 different data centre scenarios and compare its performance to two state-of-the-art Genetic Algorithms and one greedy algorithm. GENESIS produces an optimal solution for 100% of the scenarios, whereas the second-best method optimises only 71% of the scenarios. Moreover, GENESIS is the fastest among all Genetic Algorithms, averaging 15.84 minutes, compared to an average of 38.62 minutes for the second-best Genetic Algorithm.

cs.NE↗

arXiv2025

The xApp Store: A Framework for xApp Onboarding and Deployment in O-RAN

5G and beyond mobile telecommunication networks are increasingly embracing software technologies in their operation and control, similar to what has powered the growth of the cloud. This is most recently seen in the radio access network (RAN). In this new approach, the RAN is increasingly controlled by software applications known as xApps, and opens the door to third party development of xApps bringing diversity to the ecosystem, similar to mobile phone apps. This model aligns closely with the controllers in the ITU-T architecture for autonomous networks, and provides a pathway towards autonomous operation in the RAN. Unfortunately, no marketplace to host or supply xApps currently exists. This work describes our experiences in leveraging open-source O-RAN implementations to design and develop an xApp store.

cs.NI↗

arXiv2025

Automatic Generation of Digital Twins for Network Testing

The increased use of software in the operation and management of telecommunication networks has moved the industry one step closer to realizing autonomous network operation. One consequence of this shift is the significantly increased need for testing and validation before such software can be deployed. Complementing existing simulation or hardware-based approaches, digital twins present an environment to achieve this testing; however, they require significant time and human effort to configure and execute. This paper explores the automatic generation of digital twins to provide efficient and accurate validation tools, aligned to the ITU-T autonomous network architecture's experimentation subsystem. We present experimental results for an initial use case, demonstrating that the approach is feasible in automatically creating efficient digital twins with sufficient accuracy to be included as part of existing validation pipelines.

cs.NI↗

arXiv2025

BENNS: A Surrogate Model for Hybrid Online-Offline Evolution of SFC Embedding

Service Function Chains (SFCs) enable programmatic control of the functions and services in a computer network. By leveraging Software Defined Networking to control the links between virtualised network functions, SFCs provide a scalable approach to dealing with the increased pressures on network operation and management. However, embedding SFCs onto the underlying physical network and compute infrastructure is an NP-hard problem. Genetic Algorithms (GAs) have been used to address this issue, but they require significant time to evaluate solution quality (fitness) online, with most existing approaches instead adopting offline simulations or analytical evaluations. To enable online use of GAs in solving the SFC embedding problem, we introduce a hybrid online-offline approach to efficiently evaluate the fitness of generated solutions. At the core of this is BENNS: a surrogate model that approximates fitness and is agnostic to topology, traffic, and SFC-embedding. We evaluate our approach in a static environment across five experiments, varying available resources and traffic loads, and in a dynamic network environment. Our results demonstrate that our approach is capable of exploring thousands of potential configurations and generating deployable solutions in 19.1 minutes on average, compared to online-only approaches, which take 17.8 hours on average to explore ten solutions in our experiments and do not converge on an optimal solution.

cs.NI↗

arXiv2025

OpenRASE: Service Function Chain Emulation

Service Function Chains (SFCs) are one of the key enablers in providing programmable computer networks, paving the way for network autonomy. However, this also introduces new challenges, such as resource allocation and optimisation related to their operation, requiring new algorithms to address these challenges. Various tools have been used in the literature to evaluate these algorithms. However, these tools suffer from inaccuracy, low fidelity, unscalability, inflexibility, or additional code requirements. This paper introduces an emulator based on Mininet and Docker for SFCs called OpenRASE. The goal of OpenRASE is to enable the exploration of resource allocation algorithms for SFCs in a dynamic setting, allowing real CPU usage and latency to be measured. We describe the design and implementation of OpenRASE and discuss its characteristics. We also experimentally evaluate two different algorithms to address the SFC resource allocation challenge, including an online Genetic Algorithm, using OpenRASE to show its effectiveness and practicality for dynamic network conditions.

cs.NI↗

arXiv2025

A Digital Twinning Approach to Decarbonisation: Research Challenges

Transportation accounts for around 27% of green house gas emissions in the UK. While an obvious priority area for decarbonisation, and aligned to the UK government goal of reducing emissions by 68% for 2030, the free-market nature of the transportation sector combined with its fundamentally implicit and pervasive connections to all aspects of society and national infrastructure mean that all decarbonisation efforts to date have been siloed within a single transport sector, e.g. only considering greener aviation fuels. Truly decarbonising transport requires radical changes to the entire transport infrastructure, and since that transport does not happen in isolation, a single user often using multiple modes, we need a view over the whole transport system. The first step to solving a problem is to understand it. As a result of the fragmented nature of the transportation sector, there is currently no system level view. Without the ability to monitor even adjacent transport domains, the ability for people or organisations to (dynamically) adapt their operations for decarbonisation outcomes is unrealistic. As transportation is a complex social-techno-economic system, information and knowledge sharing is a must to be able to understand and explore potential solutions to the decarbonisation challenge. We believe a Federated Digital Twinning Approach has the potential to tackle transport decarbonisation problems, and, in this extended abstract, we give an overview of the research required to tackle the fundamental challenges around digital twin design, generation, validation and verification.

eess.SY↗

arXiv2025

Energy-Aware Workflow Execution: An Overview of Techniques for Saving Energy and Emissions in Scientific Compute Clusters

Scientific research in many fields routinely requires the analysis of large datasets, and scientists often employ workflow systems to leverage clusters of computers for their data analysis. However, due to their size and scale, these workflow applications can have a considerable environmental footprint in terms of compute resource use, energy consumption, and carbon emissions. Mitigating this is critical in light of climate change and the urgent need to reduce carbon emissions. In this chapter, we exemplify the problem by estimating the carbon footprint of three real-world scientific workflows from different scientific domains. We then describe techniques for reducing the energy consumption and, thereby, carbon footprint of individual workflow tasks and entire workflow applications, such as using energy-efficient heterogeneous architectures, generating optimised code, scaling processor voltages and frequencies, consolidating workloads on shared cluster nodes, and scheduling workloads for optimised energy efficiency.

cs.DC↗

arXiv2022

Network Digital Twin: Context, Enabling Technologies and Opportunities

The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators to manage their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real-time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then, we present a case study that leverages a ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks.

cs.NI↗

arXiv2022

FedFly: Towards Migration in Edge-based Distributed Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine learning technique that trains models while keeping all the original data generated on devices locally. Since devices may be resource constrained, offloading can be used to improve FL performance by transferring computational workload from devices to edge servers. However, due to mobility, devices participating in FL may leave the network during training and need to connect to a different edge server. This is challenging because the offloaded computations from edge server need to be migrated. In line with this assertion, we present FedFly, which is, to the best of our knowledge, the first work to migrate a deep neural network (DNN) when devices move between edge servers during FL training. Our empirical results on the CIFAR10 dataset, with both balanced and imbalanced data distribution, support our claims that FedFly can reduce training time by up to 33% when a device moves after 50% of the training is completed, and by up to 45% when 90% of the training is completed when compared to state-of-the-art offloading approach in FL. FedFly has negligible overhead of up to two seconds and does not compromise accuracy. Finally, we highlight a number of open research issues for further investigation. FedFly can be downloaded from https://github.com/qub-blesson/FedFly.

cs.DC↗

arXiv2022

FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning

Applying Federated Learning (FL) on Internet-of-Things devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: (i) execution on devices with limited computational capabilities, (ii) accounting for stragglers due to computational heterogeneity of devices, and (iii) adaptation to the changing network bandwidths. This paper presents FedAdapt, an adaptive offloading FL framework to mitigate the aforementioned challenges. FedAdapt accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers. Further, FedAdapt adopts reinforcement learning based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth. Experimental studies are carried out on a lab-based testbed and it is demonstrated that by offloading a DNN from the device to the server FedAdapt reduces the training time of a typical IoT device by over half compared to classic FL. The training time of extreme stragglers and the overall training time can be reduced by up to 57%. Furthermore, with changing network bandwidth, FedAdapt is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy.

cs.DC↗

arXiv2022

Digital Twin Network: Opportunities and Challenges

The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. These applications typically have stringent requirements (e.g., ultra-low deterministic latency), making it more difficult for network operators to manage their network resources efficiently. In this article, we propose the Digital Twin Network (DTN) as a key enabler for efficient network management in modern networks. We describe the general architecture of the DTN and argue that recent trends in Machine Learning (ML) enable building a DTN that efficiently and accurately mimics real-world networks. In addition, we explore the main ML technologies that enable developing the components of the DTN architecture. Finally, we describe the open challenges that the research community has to address in the upcoming years in order to enable the deployment of the DTN in real-world scenarios.

cs.NI↗

arXiv2021

Revisiting the Arguments for Edge Computing Research

This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for edge computing research even though its initial concepts were formulated more than a decade ago.

cs.DC↗

arXiv2021

Multiparty Session Types for Safe Runtime Adaptation in an Actor Language (Extended version)

Human fallibility, unpredictable operating environments, and the heterogeneity of hardware devices are driving the need for software to be able to adapt as seen in the Internet of Things or telecommunication networks. Unfortunately, mainstream programming languages do not readily allow a software component to sense and respond to its operating environment, by discovering, replacing, and communicating with components that are not part of the original system design, while maintaining static correctness guarantees. In particular, if a new component is discovered at runtime, there is no guarantee that its communication behaviour is compatible with existing components. We address this problem by using multiparty session types with explicit connection actions, a type formalism used to model distributed communication protocols. By associating session types with software components, the discovery process can check protocol compatibility and, when required, correctly replace components without jeapordising safety. We present the design and implementation of EnsembleS, the first actor-based language with adaptive features and a static session type system, and apply it to a case study based on an adaptive DNS server. We formalise the type system of EnsembleS and prove the safety of well-typed programs, making essential use of recent advances in non-classical multiparty session types.

cs.PL↗

arXiv2020

Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, for a given DNN, identifying the optimal partition configuration for distributing the DNN that maximizes performance is a significant challenge. This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning. This paper presents Scission, a tool for automated benchmarking of DNNs on a given set of target device, edge and cloud resources for determining optimal partitions that maximize DNN performance. The decision-making approach is context-aware by capitalizing on hardware capabilities of the target resources, their locality, the characteristics of DNN layers, and the network condition. Experimental studies are carried out on 18 DNNs. The decisions made by Scission cannot be manually made by a human given the complexity and the number of dimensions affecting the search space. The benchmarking overheads of Scission allow for responding to operational changes periodically rather than in real-time. Scission is available for public download at https://github.com/qub-blesson/Scission.

cs.DC↗

arXiv2020

WattsApp: Power-Aware Container Scheduling

Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensive requiring hardware level changes) for obtaining the power consumed by containers for facilitating power-aware container scheduling, an essential activity for efficient management of distributed systems. This paper presents WattsApp, a tool underpinned by a six step software-based method for power-aware container scheduling to minimize power cap violations on a server. The proposed method relies on a neural network-based power estimation model and a power capped container scheduling technique. Experimental studies are pursued in a lab-based environment on 10 benchmarks deployed on Intel and ARM processors. The results highlight that the power estimation model has negligible overheads for data collection - nearly 90% of all data samples can be estimated with less than a 10% error, and the Mean Absolute Percentage Error (MAPE) is less than 6%. The power-aware scheduling of WattsApp is more effective than Intel's Running Power Average Limit (RAPL) based power capping for both single and multiple containers as it does not degrade the performance of all containers running on the server. The results confirm the feasibility of WattsApp.

cs.DC↗

arXiv2012

The Spitzer Survey of Interstellar Clouds in the Gould Belt. V. Ophiuchus North Observed with IRAC and MIPS

We present Spitzer IRAC (2.1 sq. deg.) and MIPS (6.5 sq. deg.) observations of star formation in the Ophiuchus North molecular clouds. This fragmentary cloud complex lies on the edge of the Sco-Cen OB association, several degrees to the north of the well-known rho Oph star-forming region, at an approximate distance of 130 pc. The Ophiuchus North clouds were mapped as part of the Spitzer Gould Belt project under the working name `Scorpius'. In the regions mapped, selected to encompass all the cloud with visual extinction AV>3, eleven Young Stellar Object (YSO) candidates are identified, eight from IRAC/MIPS colour-based selection and three from 2MASS K/MIPS colours. Adding to one source previously identified in L43 (Chen et al. 2009), this increases the number of YSOcs identified in Oph N to twelve. During the selection process, four colour-based YSO candidates were rejected as probable AGB stars and one as a known galaxy. The sources span the full range of YSOc classifications from Class 0/1 to Class III, and starless cores are also present. Twelve high-extinction (AV>10) cores are identified with a total mass of approx. 100 solar masses. These results confirm that there is little ongoing star formation in this region (instantaneous star formation efficiency <0.34%) and that the bottleneck lies in the formation of dense cores. The influence of the nearby Upper Sco OB association, including the 09V star zeta Oph, is seen in dynamical interactions and raised dust temperatures but has not enhanced levels of star formation in Ophiuchus North.

astro-ph.GA↗