INSIDER

INSIDER project logo

Project Overview

Project: INSIDER

Funding: PRIN 2022, Italian Ministry of University and Research

Grant No.: 2022WWSCRR

CUP: H53D23003670006

Topics: Edge–Cloud Continuum, IoT, Intelligent Deployment, Multi-Cloud Service Recommendation, Workflow Modeling, LLM-assisted Decision Support

Introduction

INSIDER (INtelligent ServIce Deployment for advanced cloud-Edge integRation) is a research project focused on the design, analysis, and intelligent deployment of IoT applications across the edge–cloud continuum.
The project investigates methods and tools for supporting the automatic selection of suitable deployment configurations for distributed applications running on hybrid cloud/edge infrastructures.

Modern IoT applications often require low latency, scalability, privacy awareness, efficient bandwidth usage, and flexible integration across heterogeneous computing layers.
In this context, INSIDER explores how to model infrastructures and applications, define workflows and constraints, evaluate alternative deployment solutions, and support intelligent service placement decisions across cloud and edge environments.

The project adopts a platform-independent perspective, so that applications can be described first at an abstract level and then mapped onto concrete services provided by different execution environments and cloud platforms.
This enables more systematic reasoning on deployment choices and supports the development of reusable solutions for smart-city and industrial IoT scenarios.

Objectives

  • Model hybrid cloud/edge infrastructures, services, and applications in a platform-independent way.
  • Represent IoT applications as workflows of abstract services with functional and non-functional constraints.
  • Evaluate alternative deployment configurations according to metrics such as latency, bandwidth, persistence, scalability, energy-related indicators, and cost.
  • Support intelligent selection of deployment solutions through deterministic and AI-assisted strategies.
  • Validate the proposed methodology on representative application scenarios, including smart-city and industrial IoT use cases.

Research Lines

INSIDER is organized around complementary research lines that connect application modeling, service discovery, deployment reasoning, and validation across the edge–cloud continuum.

  • Platform-independent workflow modeling: abstraction of applications as workflows of services independent of the target provider and execution layer.
  • Service composition with QoS constraints: representation of functional and non-functional requirements for intelligent deployment support.
  • Deployment evaluation and selection: comparison of alternative configurations through deterministic and intelligent recommendation strategies.
  • AI-assisted service recommendation: exploration of Generative AI and Large Language Models for deployment advising and multi-cloud service mapping.
  • Validation on representative scenarios: assessment of the framework on smart-city and industrial IoT use cases.

Research Highlights

  • Definition of a platform-independent methodology for designing IoT applications on the edge–cloud continuum.
  • Development of software tools for workflow design, service matching, and deployment-oriented experimentation.
  • Investigation of deterministic and LLM-assisted strategies for intelligent service deployment.
  • Validation of the framework on smart-city mobility and industrial IoT scenarios.
  • Public release of software artifacts through the official GitHub repository.
  • Dissemination of the project results through international conferences, journals, public seminars, and online channels.

Main Results

INSIDER produced a methodological and software framework for workflow modeling, service matching, deployment advising, and deployment evaluation in heterogeneous edge–cloud environments. The project contributed to the definition of:

  • a workflow-based and platform-independent modeling approach for IoT applications;
  • a service recommendation and deployment-advising layer for mapping abstract services to concrete provider services;
  • evaluation and selection strategies for comparing alternative deployment configurations;
  • software artifacts supporting workflow design, backend solving, and execution-oriented experimentation;
  • validation studies in representative smart-city and industrial IoT scenarios.

The project also contributed to the scientific discussion on intelligent deployment across heterogeneous infrastructures by combining workflow modeling, multi-cloud service reasoning, optimization-oriented approaches, and AI-assisted recommendation techniques.

Publications

The scientific outcomes of INSIDER have been disseminated through international conferences and journals in the areas of edge/cloud computing, IoT systems, workflow modeling, deployment optimization, and AI- and LLM-assisted data analysis and service recommendation.
The project has produced a broad portfolio of publications spanning both methodological advances and application-oriented contributions.
The complete list of publications related to the project is reported below.

Software and Repository

The main software artifacts developed within the project are publicly available through the official GitHub repository:

https://github.com/SCAlabUnical/INSIDER

The repository includes:

  • a web-based workflow designer for modeling applications as compositions of abstract services;
  • a backend API server supporting service recommendation and deployment-oriented solving strategies;
  • example workflow specifications for representative use cases;
  • Docker-based support for reproducible execution and experimentation.

The repository demonstrates that INSIDER is not limited to conceptual models, but is supported by an executable and modular software platform for workflow design, service recommendation, and deployment-oriented experimentation.

Events and Dissemination

INSIDER activities have been disseminated through scientific conferences, public seminars, online communication, and project meetings.
The project also promoted discussions with researchers, students, and stakeholders interested in the edge–cloud continuum, IoT applications, and intelligent deployment strategies.

Dedicated events were organized during the project lifetime to present the project goals, intermediate progress, and final results.
Dissemination activities also included online visibility through the project webpage, the public GitHub repository, and institutional/social communication channels.

People

The project is carried out through the collaboration of researchers from the University of Calabria (UNICAL) and ICAR-CNR.

University of Calabria (UNICAL)

UNICAL is the coordinating unit of the project and leads the activities related to project management, workflow modeling, software development, intelligent deployment support, and dissemination.

  • Fabrizio Marozzo (Principal Investigator)
  • Domenico Talia
  • Paolo Trunfio
  • Eugenio Cesario
  • Cristian Cosentino
  • Alessio Orsino
  • Aleandro Presta

ICAR-CNR

The ICAR-CNR unit contributes to the methodological and experimental development of the project, with activities related to deployment reasoning, simulation, optimization, and validation.

  • Andrea Vinci (Secondary Investigator and leader of the ICAR-CNR research unit)
  • Antonio Francesco Gentile
  • Gianluigi Folino
  • Rosa Varchera

Funding Information

This project has received funding from the Italian Ministry of University and Research under the PRIN 2022 programme:

INSIDER: INtelligent ServIce Deployment for advanced cloud-Edge integRation
Grant No. 2022WWSCRR
CUP H53D23003670006


Funding acknowledgement