arXiv subjects

Nguyen Khoi Tran

Publications and source records attributed to Nguyen Khoi Tran.

6 recordsLinked to original sources

arXiv2025

A Multivocal Review of MLOps Practices, Challenges and Open Issues

MLOps has emerged as a key solution to address many socio-technical challenges of bringing ML models to production, such as integrating ML models with non-ML software, continuous monitoring, maintenance, and retraining of deployed models. Despite the utility of MLOps, an integrated body of knowledge regarding MLOps remains elusive because of its extensive scope due to the diversity of ML productionalization challenges it addresses. Whilst the existing literature reviews provide valuable snapshots of specific practices, tools, and research prototypes related to MLOps at various times, they focus on particular facets of MLOps, thus fail to offer a comprehensive and invariant framework that can weave these perspectives into a unified understanding of MLOps. This paper presents a Multivocal Literature Review that systematically analyzes a corpus of 150 peer-reviewed and 48 grey literature to synthesize a unified conceptualization of MLOps and develop a snapshot of its best practices, adoption challenges, and solutions.

cs.SE↗

arXiv2024

An Empirically Grounded Reference Architecture for Software Supply Chain Metadata Management

With the rapid rise in Software Supply Chain (SSC) attacks, organisations need thorough and trustworthy visibility over the entire SSC of their software inventory to detect risks early and identify compromised assets rapidly in the event of an SSC attack. One way to achieve such visibility is through SSC metadata, machine-readable and authenticated documents describing an artefact's lifecycle. Adopting SSC metadata requires organisations to procure or develop a Software Supply Chain Metadata Management system (SCM2), a suite of software tools for performing life cycle activities of SSC metadata documents such as creation, signing, distribution, and consumption. Selecting or developing an SCM2 is challenging due to the lack of a comprehensive domain model and architectural blueprint to aid practitioners in navigating the vast design space of SSC metadata terminologies, frameworks, and solutions. This paper addresses the above-mentioned challenge by presenting an empirically grounded Reference Architecture (RA) comprising of a domain model and an architectural blueprint for SCM2 systems. Our proposed RA is constructed systematically on an empirical foundation built with industry-driven and peer-reviewed SSC security frameworks. Our theoretical evaluation, which consists of an architectural mapping of five prominent SSC security tools on the RA, ensures its validity and applicability, thus affirming the proposed RA as an effective framework for analysing existing SCM2 solutions and guiding the engineering of new SCM2 systems.

cs.CR↗

arXiv2022

A Framework for Automating Deployment and Evaluation of Blockchain Network

Blockchain network deployment and evaluation have become prevalent due to the demand for private blockchains by enterprises, governments, and edge computing systems. Whilst a blockchain network's deployment and evaluation are driven by its architecture, practitioners still need to learn and carry out many repetitive and error-prone activities to transform architecture into an operational blockchain network and evaluate it. Greater efficiency could be gained if practitioners focus solely on the architecture design, a valuable and hard-to-automate activity, and leave the implementation steps to an automation framework. This paper proposes an automation framework called NVAL (Network Deployment and Evaluation Framework), which can deploy and evaluate blockchain networks based on their architecture specifications. The key idea of NVAL is reusing and combining the existing automation scripts and utilities of various blockchain types to deploy and evaluate incoming blockchain network architectures. We propose a novel meta-model to capture blockchain network architectures as computer-readable artefacts and employ a state-space search approach to plan and conduct their deployment and evaluation. An evaluative case study shows that NVAL successfully combines seven deployment and evaluation procedures to deploy 65 networks with 12 different architectures and generate 295 evaluation datasets whilst incurring a negligible processing time overhead.

cs.SE↗

arXiv2022

ProML: A Decentralised Platform for Provenance Management of Machine Learning Software Systems

Large-scale Machine Learning (ML) based Software Systems are increasingly developed by distributed teams situated in different trust domains. Insider threats can launch attacks from any domain to compromise ML assets (models and datasets). Therefore, practitioners require information about how and by whom ML assets were developed to assess their quality attributes such as security, safety, and fairness. Unfortunately, it is challenging for ML teams to access and reconstruct such historical information of ML assets (ML provenance) because it is generally fragmented across distributed ML teams and threatened by the same adversaries that attack ML assets. This paper proposes ProML, a decentralised platform that leverages blockchain and smart contracts to empower distributed ML teams to jointly manage a single source of truth about circulated ML assets' provenance without relying on a third party, which is vulnerable to insider threats and presents a single point of failure. We propose a novel architectural approach called Artefact-as-a-State-Machine to leverage blockchain transactions and smart contracts for managing ML provenance information and introduce a user-driven provenance capturing mechanism to integrate existing scripts and tools to ProML without compromising participants' control over their assets and toolchains. We evaluate the performance and overheads of ProML by benchmarking a proof-of-concept system on a global blockchain. Furthermore, we assessed ProML's security against a threat model of a distributed ML workflow.

cs.SE↗

arXiv2022

Mod2Dash: A Framework for Model-Driven Dashboards Generation

The construction of an interactive dashboard involves deciding on what information to present and how to display it and implementing those design decisions to create an operational dashboard. Traditionally, a dashboard's design is implied in the deployed dashboard rather than captured explicitly as a digital artifact, preventing it from being backed up, version-controlled, and shared. Moreover, practitioners have to implement this implicit design manually by coding or configuring it on a dashboard platform. This paper proposes Mod2Dash, a software framework that enables practitioners to capture their dashboard designs as models and generate operational dashboards automatically from these models. The framework also provides a GUI-driven customization approach for practitioners to fine-tune the auto-generated dashboards and update their models. With these abilities, Mod2Dash enables practitioners to rapidly prototype and deploy dashboards for both operational and research purposes. We evaluated the framework's effectiveness in a case study on cyber security visualization for decision support. A proof-of-concept of Mod2Dash was employed to model and reconstruct 31 diverse real-world cyber security dashboards. A human-assisted comparison between the Mod2Dash-generated dashboards and the baseline dashboards shows a close matching, indicating the framework's effectiveness for real-world scenarios.

cs.HC↗

arXiv2018

Internet of Things Search Engine: Concepts, Classification, and Open Issues

This article focuses on the complicated yet still relatively immature area of the Internet of Things Search Engines (IoTSE). It introduces related concepts of IoTSE and a model called meta-path to describe and classify IoTSE systems based on their functionality. Based on these concepts, we have organized the research and development efforts on IoTSE into eight groups and presented the representative works in each group. The concepts and ideas presented in this article are generated from an extensive structured study on over 200 works spanning over one decade of IoTSE research and development.

cs.IR↗