Over the last few decades, information technologies in the field of computing systems and software architectures have evolved to provide companies with robust solutions to run their business logic. The transition from monolithic applications to Service-Oriented Architecture (SOA) and micro-services represents a fundamental shift in the way companies design, build, and deploy their software. This evolution has been driven by the need for greater scalability, flexibility, and agility in response to the dynamic and demanding landscape of modern application requirements. As software development continues to evolve, the incorporation of intelligent agents has become widespread to provide smartness within distributed system as well as to design and manage complex scenarios, such as the device-edge-cloud continuum and IoT ecosystems. This paper explores the role of intelligent agents within SOA and micro-services architectures, highlighting the spectrum of benefits and limitations they bring to the development life cycle. We also discuss challenges common to both architectures and identify solutions that each architecture provides with respect to the other. Finally, to overcome some of these challenges, a new approach, stemmed in the context of the EU funded MLSysOps Project, is presented; the proposed approach utilizes agents and Machine Learning (ML) as-a-service to provide inbound and outbound intelligence in the system.

Agents in Software Development Architectures

Savaglio C.;Arijo N. H.;Aloi G.;Fortino G.;Gravina R.
2023-01-01

Abstract

Over the last few decades, information technologies in the field of computing systems and software architectures have evolved to provide companies with robust solutions to run their business logic. The transition from monolithic applications to Service-Oriented Architecture (SOA) and micro-services represents a fundamental shift in the way companies design, build, and deploy their software. This evolution has been driven by the need for greater scalability, flexibility, and agility in response to the dynamic and demanding landscape of modern application requirements. As software development continues to evolve, the incorporation of intelligent agents has become widespread to provide smartness within distributed system as well as to design and manage complex scenarios, such as the device-edge-cloud continuum and IoT ecosystems. This paper explores the role of intelligent agents within SOA and micro-services architectures, highlighting the spectrum of benefits and limitations they bring to the development life cycle. We also discuss challenges common to both architectures and identify solutions that each architecture provides with respect to the other. Finally, to overcome some of these challenges, a new approach, stemmed in the context of the EU funded MLSysOps Project, is presented; the proposed approach utilizes agents and Machine Learning (ML) as-a-service to provide inbound and outbound intelligence in the system.
2023
device-edge-cloud continuum
microservices
MLSysOps
multi agent systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/366154
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