AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Graziano, M.; Cante, L. Colucci; Martino, B. Di
Deploying Large Language Model on Cloud-Edge Architectures: A Case Study for Conversational Historical Characters Book Section
In: Lecture Notes on Data Engineering and Communications Technologies, vol. 250, pp. 196–205, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23674512 (ISSN).
Abstract | Links | BibTeX | Tags: Agent based, Augmented Reality, Case-studies, Chatbots, Cloud computing architecture, Conversational Agents, EDGE architectures, Historical characters, Language Model, Modeling languages, Real time performance, WEB application, Web applications, Work analysis
@incollection{graziano_deploying_2025,
title = {Deploying Large Language Model on Cloud-Edge Architectures: A Case Study for Conversational Historical Characters},
author = {M. Graziano and L. Colucci Cante and B. Di Martino},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002995405&doi=10.1007%2f978-3-031-87778-0_19&partnerID=40&md5=c54e9ce66901050a05de68602e4a8266},
doi = {10.1007/978-3-031-87778-0_19},
isbn = {23674512 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lecture Notes on Data Engineering and Communications Technologies},
volume = {250},
pages = {196–205},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This work analyzes the deployment of conversational agents based on large language models (LLMs) in cloud-edge architectures, placing emphasis on scalability, efficiency and real-time performance. Through a case study, we present a web application that allows users to interact with an augmented reality avatar that impersonates a historical character. The agent, powered by an LLM delivers immersive and contextually coherent dialogues. We discuss the solutions adopted to manage latency and distribute the computational load between the cloud, which takes care of language processing, and the edge nodes, ensuring a smooth user experience. The results obtained demonstrate how accurate design can optimize the use of LLMs in distributed environments, offering advanced and high-performance interactions even in applications with high reactivity and customization requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Agent based, Augmented Reality, Case-studies, Chatbots, Cloud computing architecture, Conversational Agents, EDGE architectures, Historical characters, Language Model, Modeling languages, Real time performance, WEB application, Web applications, Work analysis},
pubstate = {published},
tppubtype = {incollection}
}
This work analyzes the deployment of conversational agents based on large language models (LLMs) in cloud-edge architectures, placing emphasis on scalability, efficiency and real-time performance. Through a case study, we present a web application that allows users to interact with an augmented reality avatar that impersonates a historical character. The agent, powered by an LLM delivers immersive and contextually coherent dialogues. We discuss the solutions adopted to manage latency and distribute the computational load between the cloud, which takes care of language processing, and the edge nodes, ensuring a smooth user experience. The results obtained demonstrate how accurate design can optimize the use of LLMs in distributed environments, offering advanced and high-performance interactions even in applications with high reactivity and customization requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.