SLICES Data Management Infrastructure for Reproducible
Experimental Research on Digital Technologies
Yuri Demchenko, Paola Grosso, Shashank Shrestha (University of Amsterdam)
Different Types of Data for Different Experimental Studies
Data Types in SLICES-RI Experimental Studies
General experimental studies and data:
Experiment data & Infra Mngt dataIndus.
verticals
demandAI-centricDIs

Design patterns, API management,
configuration, evaluation data

Data for ML algorithm
and optimisation

General experimental studies and data documentation andpublication• FAIR (Findable, Accessible, Interoperable, Reusable) dataprinciples are key for experimental data sharing• Metadata profiles to be defined for major types of experiments
and supported by data and metadata management tools• Infrastructure management information to be recorded asexperiments environment• Research Object (RO) and RO-Crate archiving template• FAIR Digital Object (FDO) and PID infrastructure• SLICES FDO profile (SFDO) and Metadata profile
Data produced for AI/ML algorithms training for smart infrastructure
optimisation and management (including energy efficiency,performance, resilience, sustainability)• Data modelling and data lineage (staging documenting)• AI/ML models serialization and portability
New Digital Infrastructure architecture elements and designpatterns• Infrastructure and design patterns• Metadata for API description, identification, composability

Cross-prop.

Cloud-toEdge
scalableDIs

Humancentric DIs

6G

5.
Models and Data for monitoring andoptimisation

Simultaneous but progressive exploration of research topics

Breaking down in priority research topics

SLICES Experimental Data Lifecycle and Research Workflow
DMI Infra services: Registries,
Storage, PID, FDO

Data Types
Data Models

Metadata

SLICES Data Storage and Management Infrastructure

Experiment
DescriptionData Curation &Quality Assurance(multi-purpose: experimental, technological, archival)

Experiment
Plan &Implement

Researcher

Data Analysis
Model Building

Data Collection& Preparation

Data Publishing andArchiving on EOSC Fed
Data Sharing
Infrastructure

Data Archiving &Publication/Sharing

EOSC Data
Archiving &Sharing(Zenodo)Rescher Tools:
Experim Mngt,Metadata, Data

Data Discovery (for experiment planning and operation)

Focus on repeatability and reproducibility with the future
support of replicability

Experiment/Data
Lifecycle Stages

Generic Experiment Model for Reproducibility

FAIR Data and Software: Storage, Registries, Tools

Experiment Description and Data Modeling: Metadata Requirements
SLICES Experimental Data/Metadata groups(1) Architecture and services(2) General Metadata definition and management(3) Experiment description and metadata(4) Domain specific (e.g. SLICES Blueprint Architecture)(5) Metadata Management tools

Experiment Control
and Monitoring

Test Generator

Data Types in Experiment operation

What metadata should describe• Experiment Variables• Configuration (equipment, DUT, Test
Generator)• Environment• Test data
• Measurement data (data model, metadata)• Orchestration and workflow• Storage API/schema• Data models: storage, databases, metadata• Experiment• Orchestration; configuration; equipment:
DUT, test generators, measurement; datastorage; data models/metadata• Dataflow: Stages, transformations,lineage/provenance, data models• Workflow: Stages, Operations/conditions,workstations

Exp/Test
Param

PortalCatalog

VRI

Data Management
Services/Tools

DGvP

DMP

Workflow(Exp&DataAnalys)Experiment Workflow
Management & Execution
A

B

C

D

Visualisa
tion

Experiment
MngmntE

Experiment
Data Ingest

Metadata(Registry&Tools)

Data, PID, FDO

Experiment Data Ingest
Data Processing
ML/Data Analytics

Data Storage, Compute, Network (cloud based)
ComputeStorage

NetworkDGvP – Data Governance Policy DMP – Data Management Plan

DataModel/M
etadata

Measurement

Data Collection

Environment
EnvironParam

DataProcessing

Storage &Phy Infra

Data Management Infrastructure Layers to separate data management and governanceconcerns and actors/roles
Layer 4 - Experiment Infrastructure configuration and management
Layer 3 - Experimental data collection/recording• Data models, metadata
Layer 2 - Data processing• Data analysis, Process/ML models building, portabilityLayer 1 - Data Storage, Archiving, Exchange• Datasets, metadata publication
Data Management Services and Tools (Data Management Plane)• Data Management Plan and Data Quality Assurance, FAIR compliance• Metadata registries and tools• Data Security and Data protection, GDPR

References
SLICES-RI - https://slices-ri.eu/ SLICES-PP - https://www.fairsfair.eu/ GreenDIGIT http://www.greendigit-project.eu/
Yuri Demchenko, et al, SLICES Data Management Infrastructure for Reproducible Experimental Research on Digital Technologies,
Proceedings Workshop on FutureG Experimental Test Platforms for Advanced Systems Implementation and Research, IEEE Global
Communications Conference, 4–8 December 2023, Kuala Lumpur, Malaysia
Fdida, Serge, et Al, SLICES, a scientific instrument for the networking community, Computer Communications, 2022, ISSN 0140-3664,
https://doi.org/10.1016/j.comcom.2022.07.019

GreenDIGIT
Grant ID: 951850

Data Processing/Visualisation

Data Management Infrastructure Layers
Layers

Virtual Research Infrastructure(Virtual Research Teams)

DUT/SUT(Device/System Under
Test)

Measurem
ConfigMetadata(names&semantics)

Experimental Data Management Infrastructure
Researcher/PI(Project Team)

DUT/SUT
Config&Setup

Contact: Yuri Demchenko <y.demchenko@uva.nl>

Grant ID: 101131207