Related papers: Service-oriented high level architecture
The cloud is a new paradigm that is paving the way for new approaches and standards. The architectural styles are evolving in response to the cloud's requirements. In recent years, microservices have emerged as the preferred architectural…
Large Language Models (LLMs) are accelerating the shift from an Internet of information to an Internet of Agents (IoA), where autonomous entities discover services, negotiate, execute tasks, and exchange value. Yet today's agents are still…
In this article, the authors introduce the main ideas around the governance of cross-Cloud application deployment and their related concepts. It is argued that, due to the increasing complexity and nature of the Cloud market, an…
As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced…
The trends of design and development of information systems have undergone a variety of ongoing phases and stages. These variations have been evolved due to brisk changes in user requirements and business needs. To meet these requirements…
Enterprise interest in multi-agent systems has shifted from generic software agents to large-language-model (LLM) based intelligent agents that plan, use tools, maintain contextual memory, inspect intermediate results, collaborate with…
Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical organization of real\text{-}world concepts that evolves both…
Big Data are rapidly produced from various heterogeneous data sources. They are of different types (text, image, video or audio) and have different levels of reliability and completeness. One of the most interesting architectures that deal…
We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data.…
This paper provides an overview of enhanced network services, while emphasizing on the role of Unmanned Aerial Vehicles (UAVs) as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various deployment…
Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private data. Existing wireless FL works primarily adopt two communication strategies: (1)…
Microservice architectures (MA) are composed of loosely coupled, course-grained services that emphasise resilience and autonomy, enabling more scalable applications to be developed. Such architectures are more tolerant of changing demands…
This work introduces a novel, modular, layered web based platform for managing machine learning experiments on grid-based High Performance Computing infrastructures. The coupling of the communication services offered by the grid, with an…
Multitier programming languages reduce the complexity of developing distributed systems by developing the distributed system in a single coherent code base. The compiler or the runtime separate the code for the components of the distributed…
Recent multi-agent frameworks built upon large language models (LLMs) have demonstrated remarkable capabilities in complex task planning. However, in real-world enterprise environments, business workflows are typically composed through…
Federated Learning has recently been utilized to collaboratively fine-tune foundation models across multiple clients. Notably, federated low-rank adaptation LoRA-based fine-tuning methods have recently gained attention, which allows clients…
Edge computing decentralizes computing resources, allowing for novel applications in domains such as the Internet of Things (IoT) in healthcare and agriculture by reducing latency and improving performance. This decentralization is achieved…
We introduce Orla, a library for constructing and running LLM-based agentic systems. Modern agentic applications consist of workflows that combine multiple LLM inference steps, tool calls, and heterogeneous infrastructure. Today, developers…
The evolution of Web and service technologies has led to a wide landscape of standards and protocols for interaction between loosely coupled software components. Examples range from Web applications, mashups, apps, and mobile devices to…
Federated learning is an emerging machine learning paradigm that enables multiple devices to train models locally and formulate a global model, without sharing the clients' local data. A federated learning system can be viewed as a…