SEArch: an execution infrastructure for service-based software systems
- URL: http://arxiv.org/abs/2404.19633v1
- Date: Tue, 30 Apr 2024 15:35:03 GMT
- Title: SEArch: an execution infrastructure for service-based software systems
- Authors: Carlos G. Lopez Pombo, Pablo Montepagano, Emilio Tuosto,
- Abstract summary: We develop SEArch, a language-independent execution infrastructure capable of performing transparent dynamic reconfiguration of software artefacts.
Choreographic mechanisms are used in SEArch to specify interoperability contracts, thus providing the support needed for automatic discovery and binding of services at runtime.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The shift from monolithic applications to composition of distributed software initiated in the early twentieth, is based on the vision of software-as-service. This vision, found in many technologies such as RESTful APIs, advocates globally available services cooperating through an infrastructure providing (access to) distributed computational resources. Choreographies can support this vision by abstracting away local computation and rendering interoperability with message-passing: cooperation is achieved by sending and receiving messages. Following this choreographic paradigm, we develop SEArch, after Service Execution Architecture, a language-independent execution infrastructure capable of performing transparent dynamic reconfiguration of software artefacts. Choreographic mechanisms are used in SEArch to specify interoperability contracts, thus providing the support needed for automatic discovery and binding of services at runtime.
Related papers
- Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN [7.375775031391254]
This paper systematically identifies and categorizes critical attributes influencing AI service orchestration in 6G networks.
We introduce an open-source, LLM-assisted toolchain that automates service packaging, deployment, and runtime profiling.
arXiv Detail & Related papers (2025-04-13T16:40:58Z) - Adopting Large Language Models to Automated System Integration [0.0]
We introduce a software architecture for automated service composition using Large Language Models (LLMs)
We propose a novel natural language query-based benchmark for service discovery.
We extend the benchmark to complete service composition scenarios.
arXiv Detail & Related papers (2025-04-11T12:42:01Z) - End-to-End Edge AI Service Provisioning Framework in 6G ORAN [7.6934511825411045]
This paper proposes a novel Edge AI and Network Service Orchestration framework that leverages Large Language Model (LLM) agents deployed as O-RAN rApps.
The proposed LLM-agent-powered system enables interactive and intuitive orchestration by translating the user's use case description into deployable AI services and corresponding network configurations.
arXiv Detail & Related papers (2025-03-15T00:48:50Z) - Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation [51.53221300103261]
This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture.
A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions.
Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection.
arXiv Detail & Related papers (2025-02-12T09:01:25Z) - Asynchronous Tool Usage for Real-Time Agents [61.3041983544042]
We introduce asynchronous AI agents capable of parallel processing and real-time tool-use.
Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting.
This work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.
arXiv Detail & Related papers (2024-10-28T23:57:19Z) - Large Language Models meet Network Slicing Management and Orchestration [0.3644165342767221]
This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems.
We discuss the challenges associated with implementing this framework and potential solutions to mitigate them.
arXiv Detail & Related papers (2024-03-20T16:29:52Z) - Serving Deep Learning Model in Relational Databases [70.53282490832189]
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains.
We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks.
The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
arXiv Detail & Related papers (2023-10-07T06:01:35Z) - A comparison between traditional and Serverless technologies in a
microservices setting [0.0]
This study implements 9 prototypes of the same microservice application using different technologies.
We use Amazon Web Services and start with an application that uses a more traditional deployment environment (Kubernetes)
Migration to a serverless architecture is performed by combining and analysing the impact (both cost and performance) of the use of different technologies such as AWS ECS Fargate, AWS, DynamoDBDB.
arXiv Detail & Related papers (2023-05-23T10:56:28Z) - COVINS-G: A Generic Back-end for Collaborative Visual-Inertial SLAM [13.190581566723917]
Collaborative SLAM is at the core of perception in multi-robot systems.
CoVINS-G is a generalized back-end building upon the COVINS framework.
We show on-par accuracy with state-of-the-art multi-session and collaborative SLAM systems.
arXiv Detail & Related papers (2023-01-17T19:23:54Z) - Composing Complex and Hybrid AI Solutions [52.00820391621739]
We describe an extension of the Acumos system towards enabling the above features for general AI applications.
Our extensions include support for more generic components with gRPC/Protobuf interfaces.
We provide examples of deployable solutions and their interfaces.
arXiv Detail & Related papers (2022-02-25T08:57:06Z) - A Data-Centric Framework for Composable NLP Workflows [109.51144493023533]
Empirical natural language processing systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components.
We establish a unified open-source framework to support fast development of such sophisticated NLP in a composable manner.
arXiv Detail & Related papers (2021-03-02T16:19:44Z) - Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn.
We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
arXiv Detail & Related papers (2020-12-01T11:46:03Z) - Large-Scale Intelligent Microservices [24.99695289157708]
We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives.
We provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis.
arXiv Detail & Related papers (2020-09-17T03:38:28Z) - A Privacy-Preserving Distributed Architecture for
Deep-Learning-as-a-Service [68.84245063902908]
This paper introduces a novel distributed architecture for deep-learning-as-a-service.
It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
arXiv Detail & Related papers (2020-03-30T15:12:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.