BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture

Salvatore Vitabile, Simone Sante Ruffo, Leonard Barolli, Vincenzo Conti

Risultato della ricerca: Article

3 Citazioni (Scopus)

Abstract

Today we live in a world of digital objects and digital technology; industry and humanities as well as technologies are truly in the midst of a digital environment driven by ICT and cyber informatics. A digital ecosystem can be defined as a digital environment populated by interacting and competing digital species. Digital species have autonomous, proactive and adaptive behaviors, regulated by peer-to-peer interactions without central control point. An interconnecting architecture with few highly connected nodes (hubs) and many low connected nodes has a scale- free architecture. A new bio-inspired analysis methodology (BIAM) environment, an investigation strategy for information flow, fault and error tolerance detection in digital ecosystems based on a scale-free architecture is presented in this paper. In order to extract the information about modules and digital species role, the analysis methodology, inspired by metabolic network working, implements a set of three interacting techniques, i.e., topological analysis, flux balance analysis and extreme pathway analysis. Highly connected nodes, intermodule connectors and ultra-peripheral nodes can be identified by evaluating their impact on digital ecosystems behavior and addressing their strengthen, fault tolerance and protection countermeasures. Two real case studies of ecosystems have been analyzed in order to test the functionalities of the proposed (BIAM) environment and the goodness of this approach.
Lingua originaleEnglish
pagine (da-a)1133-1150
Numero di pagine18
RivistaSoft Computing
Volume23
Stato di pubblicazionePublished - 2019

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Ecosystem
Ecosystems
Methodology
Fault tolerance
Vertex of a graph
Fluxes
Architecture
Adaptive Behavior
Connector
Metabolic Network
Control Points
Countermeasures
Information Flow
Industry
Peer to Peer
Fault Tolerance
Tolerance
Pathway
Extremes
Fault

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cita questo

BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture. / Vitabile, Salvatore; Ruffo, Simone Sante; Barolli, Leonard; Conti, Vincenzo.

In: Soft Computing, Vol. 23, 2019, pag. 1133-1150.

Risultato della ricerca: Article

Vitabile, Salvatore ; Ruffo, Simone Sante ; Barolli, Leonard ; Conti, Vincenzo. / BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture. In: Soft Computing. 2019 ; Vol. 23. pagg. 1133-1150.
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