KTWIN: A Serverless Kubernetes-based Digital Twin Platform

2024-08-03

arXiv.org

AI Summary

Digital twin platforms face challenges in cloud-edge collaborative environments, including high development complexity, low resource utilization, and inconsistent cross-environment deployment. To address these issues, this paper proposes a Kubernetes-native serverless digital twin platform. It pioneers the deep integration of the serverless paradigm into digital twin architecture by decoupling model abstraction from underlying infrastructure. The platform supports standardized modeling via DTDL/OpenAPI, low-code configuration, and elastic deployment. Leveraging Knative and the cloud-native toolchain, it enables millisecond-scale auto-scaling and a unified runtime across cloud and edge environments. Experimental evaluation demonstrates that, compared to conventional approaches, the platform reduces resource costs by 60–80% while preserving usability and horizontal scalability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Planning with Language Models
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

Digital Twins (DTs) systems are virtual representations of physical assets allowing organizations to gain insights and improve existing processes. In practice, DTs require proper modeling, coherent development and seamless deployment along cloud and edge landscapes relying on established patterns to reduce operational costs. In this work, we propose KTWIN a Kubernetes-based Serverless Platform for Digital Twins. KTWIN was developed using the state-of-the-art open-source Cloud Native tools, allowing DT operators to easily define models through open standards and configure details of the underlying services and infrastructure. The experiments carried out with the developed prototype show that KTWIN can provide a higher level of abstraction to model and deploy a Digital Twin use case without compromising the solution scalability. The tests performed also show cost savings ranging between 60% and 80% compared to overprovisioned scenarios.

Problem

• Digital Twin Platform
• Cloud Environment
• Cost Reduction

Innovation

• Serverless Architecture
• Kubernetes-based Digital Twins
• Cost Efficiency