Work package 1:
The context-aware Post-IP
architecture
| Workpackage chair | LIP6 |
| Partners | Devoteam, Netcenter, Ginkgo, PUC, Telecom SudParis, UFRJ, Unicamp |
Piloting planes that will be finalized in work package
3.
More precisely, the Horizon platform will be provided
by a multi-agent system to offer some intelligence. The multi-agent
system is
formed with agents situated in all network equipment (common to all
virtual
instances).
Agents will be based on Ginkgo technology but a large
number of improvements have to be provided through the Horizon project.
The
Horizon agent is shown in Figure 7.
The Behaviours
are autonomic software components permanently adapting themselves to
the
environment changes. Each of these Behaviours can be considered as a
specialized function with some expert capabilities.
Each Behaviour is essentially a sense->decide->act
loop. Typical categories of Behaviours are as follows:
• Producing knowledge for the Situated View in
cooperation with other agents.
• Reasoning individually or collectively to evaluate
the situation and decide to apply an appropriate action,
e.g. a Behaviour can simply be in charge of computing
bandwidth availability on the network equipment (NE).
It can also regularly perform a complex diagnostic
scenario or it can be dedicated to automatic recognition of specific
network
conditions.
• Acting onto the NE parameters, e.g. Behaviour can
tune QoS parameters in a DiffServ context.
Behaviours have access to the Situated View which
operates within each agent as a whiteboard shared among the agent's
Behaviours.
The fourth entity, the rule engine,
can be seen as a specific Behaviour
that can or cannot be used depending on the memory space
and real time constraints. The rule engine exploits the tolerance for
imprecision and learning capabilities. At this juncture, the principal
constituents are Fuzzy Logic, Neural Computing, Evolutionary
Computation
Machine Learning and Probabilistic Reasoning.
The activation, dynamic parameterization and
scheduling of Behaviours (the rule engine is seen as a behaviour)
within an
agent is performed by the Dynamic
Planner. The Dynamic Planner decides which Behaviours have to
be active,
when they have to be active and with which parameters. The Dynamic
Planner detects
changes in the Situated View and occurrence of external/internal
events; from
there, it pilots the reaction of the agent to changes in the network
environment.
Active
context-aware infrastructure introduces
enriched context sources with techniques that will provide smart
context
information delivery. A context level agreement protocol will be
explored to
provide automatic context matching with the user’s profile, terminal
capabilities and service requirements and offering. A particular
attention will
be devoted to the pro-active aspect of the smart context with an
appropriate
context dissemination protocol. The primary aim of the protocol is the
adaptive
distribution of context information among multiple mobile and fixed
sources and
destinations (e.g. devices, service components) using (negotiated)
specific
dissemination attribute such as power saving and cost. Context
dissemination
can be
The goal of this task 3 is to develop the
Horizon agent and the Behaviours (and rule engine) dedicated to bring
some
intelligence using AI technologies. This intelligence will be used in
WP3 to
realize the piloting system able to manage & control virtual
networks, and
the allocation of physical resources to the different virtual networks.
The
Dynamic Planner and the Behaviours dedicated to the piloting system
(piloting
algorithms) will be developed in WP3.
Post-IP the task
Deadline Leader