Users and services in intelligent networks

  • Authors:
  • Erol Gelenbe

  • Affiliations:
  • Memb. Acad. Europ., FIEE FIEEE FACM, Dennis Gabor Chair, Intelligent Systems and Networks Group, Electrical & Electronic Engineering Dept., Imperial College, London

  • Venue:
  • AINTEC'05 Proceedings of the First Asian Internet Engineering conference on Technologies for Advanced Heterogeneous Networks
  • Year:
  • 2005

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Abstract

We present a vision of an Intelligent Network in which users dynamically indicate their requests for services, and formulate needs in terms of Quality of Service (QoS) and price. Users can also monitor on-line the extent to which their requests are being satisfied. In turn the services will dynamically try to satisfy the user as best as they can, and inform the user of the level at which the requests are being satisfied, and at what cost. The network will provide guidelines and constraints to users and services, to avoid that they impede each others’ progress. This intelligent and sensible dialogue between users, services and the network can proceed constantly based on mutual observation, network and user self-observation, and on-line adaptive and locally distributed feedback control which proceeds at the same speed as the traffic flows and events being controlled. We review issues such as network “situational awareness”, self-organisation, and structure, and relate these concepts to the ongoing research on autonomic communication systems. We relate the search for services in the network to the question of QoS and routing. We examine the need to dynamically protect the networked system from denial of service (DoS) attacks, and propose an approch to DoS defence which uses the detection of violations of QoS constraints and the automatic throttling or dropping of traffic to protect critacl nodes. We also discuss how this vision of an Intelligent Network can benefit from techniques that have been experimented in the Cognitive Packet Network (CPN) test-bed at Imperial College, thanks to “smart packets” and reinforcement learning, which offers routing that is dynamically modified using on on-line sensing and monitoring, based on users’ QoS needs and overall network objectives.