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Digital Ecosystems A (Rather) New Vision of IT

Explore the characteristics, models, and applications of digital ecosystems in this course. Learn about autonomic systems, emerging applications, and the security and privacy issues surrounding digital ecosystems.

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Digital Ecosystems A (Rather) New Vision of IT

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  1. Digital EcosystemsA (Rather) New Vision of IT Lionel Brunie National Institute of Applied Sciences (INSA) LIRIS Laboratory/DRIM Team – UMR CNRS 5205 Lyon, France http://liris.cnrs.fr/lionel.brunie

  2. Contents of the Course • Definition and Characteristics • Distributed Systems Models • Autonomic Systems • Digital Ecosystems • Cyberspace and Digital Ecosystem(s) • Use case – Emerging Applications • Multi-scale Ego-centric Ubiquitous Digital Ecosystem • Security and Privacy Issues

  3. Digital EcosystemDefinition and Characteristics

  4. Digital Ecosystems… A very versatile metaphor! • IT industry, Economy, Business • SOA, Software Engineering • Networks and Information Systems • For us: Distributed Collaborative Systems

  5. Basic Models of Distributed Systems • Client-Server (typically, the Web) • Peer-to-Peer (typically Bittorent and file sharing systems) • Grid (typically, the CERN LCG) • Mobile agents • Variants → Course on large scale computing

  6. The New Frontier • Traditional models fail to model and implement highly dynamic loosely supervised distributed systems • Alternative models • autonomic computing → focus on autonomy and coordination • cloud computing → re-centralize everything • pervasive/ubiquitous computing → focus on user context • Internet of Things → focus on interoperability • digital ecosystems → an holistic vision

  7. Autonomic Computing and Digital Ecosystems: towards collaborative systems • Autonomic Computing [Horn, 2001; Parashar and Hariri, 2005] • analogy with the nervous system – notion of equilibrium • observation: emerging systems and applications are dynamic • survivability of the system  the system can adapt to environment changes (incl. attacks, faults, disruptions…) • basic operation loop of an autonomic system: Monitor-Decide-Adapt • sense / monitor the environment (context discovery), and analyze the context • plan a knowledge-based adaptation of the system (decision making) • execute the change • context- and self-awareness

  8. Architecture of an autonomic agent KE: Knowledge Engine M&A: Monitoring and Analysis Cardinals: performance, configuration, protection, security L/G: local and global control loops S: stable state A: adapted state E: execute action From Parashar and Hariri, 2005

  9. Autonomic Computing and Digital Ecosystems: towards collaborative systems • Autonomic Computing [Horn, 2001; Parashar and Hariri, 2005] (cont’d): characteristics/properties of a generic autonomic system • Self Configuring • Self Optimizing • Self-Healing • Self Protecting • Context Aware • Open • Anticipatory • Proactive

  10. Autonomic Computing and Digital Ecosystems: towards collaborative systems • Digital Ecosystems (Distributed Collaborative Systems)[Boley et al., 2007; Damiani and his group @ Milan] • “A digital ecosystem can be defined as an open, loosely coupled, domain clustered, demand-driven, self-organizing agent environment, where each agent of each species is proactive and responsive regarding its own benefit/profit but is also responsible to its system.” (Boley and Chang, 2007)

  11. Autonomic Computing and Digital Ecosystems: towards collaborative systems • Digital Ecosystems: Main Characteristics • Loose coupling - Personal Engagement • Equilibrium – Interdependence - Balance • Local Interactions  Global Behavior • Self-organization – Autonomy - No Central or Distributed Control • Adaptation to the Environment – Dynamicity – Evolutionary System • Collective (Swarm) Intelligence – Structured Relationship - Responsibility • Openness - Multiplicity of Ecosystems (cf. human social life)

  12. Autonomic Computing and Digital Ecosystems: towards collaborative systems • Digital Ecosystems: Main Characteristics (cont’d) • Cooperation – Collective/Swarm Intelligence • cf. bees, ants, dolphins… • swarm is a set of agents that can interact and that share a common interest • collective problem solving • Communication  System Semantics • DE => need of shared explicit formal semantics (formal languages) • Link with some characteristics of the semantic Web • A new way of designing/thinking distributed systems and applications • Related to autonomic computing

  13. Is the “Cyberspace” a (set of) Digital Ecosystem(s)?(can this concept helps us to understand our digital world?)

  14. Multi-scale Ego-centric Ubiquitous Digital Ecosystem • Ego-centric • focus on the user’s interactions with her/his environment(s) • personalization – context-awareness • Ubiquitous • mobility • simultaneous interactions with multiple ecosystems • Multi-scale • comprise entities (typically, services) of totally different nature, origin and operational characteristics • from an embedded “thing” to a public cloud • integration of data, information, knowledge from all sources • huge mass of information • Digital Ecosystem • see above

  15. Back to the “Visions” (Part 1 of the Course) • Seamless “weaved into the fabric of everyday life” • “Graceful integration” • Transparency of the “cyber infrastructure” (“vanish in the background”) • User-centric • Conclusion: hard to imagine in 1991 – realistic as an objective for the next decade

  16. OK, it is not a dreambut…Is it a nightmare ?

  17. Multi-scale Ego-centric Ubiquitous Digital Ecosystem:Security and Privacy Issues • You are the hub and the source of information  (supposed to be) sensitive personal information • Data exchanges, dissemination of information between multiple ecosystems with various security and privacy characteristics  un-alignment of security/privacy policies  sensitive information leakage • You do not control, worse do not actually know, the environment  Uncertainty  Dynamicity  Unpredictability  Absence of trust, Anonymity • Big Brother can watch you,now!  Your everyday life is seamlessly weaved into the cyberspace fabric: you are traced  The cyberspace does not forget: traces cannot be deleted  The storage and processing capacities are almost unlimited: your traces are/can be mined • See course on these issues

  18. Conclusion • New technologies enable / need / argue for new models, new designs • Whatever the model, some basic features • Autonomy • Collaboration • User-Centricity • Integration • Context-Awareness • Mobility • Digital ecosystems provide a holistic vision of emerging digital environments • Some still largely open issues, esp. regarding interoperability • The cyberspace as a digital ecosystem is the Babel Tower • A fantastic, however in some way dreadful set of opportunities for new applications

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