Work package 3: The piloting plane

Workpackage chair UFRJ
Partners Devoteam, LIP6, Netcenter, Ginkgo, PUC, Telecom SudParis, Unicamp

 The work package objective is to define the piloting system necessary for handling all the problems arising in the network: congestion, failure, QoS problem, etc. The first task will be oriented on the knowledge. The second task will focus on how the piloting plane is recovering the best possible knowledge adapted to the control and managements algorithms. The process for deciding what knowledge has to be recovered, where to find this knowledge and when to retrieve this knowledge will also be studied.
 Networks represent a very dynamic and complex area, in which every day managers are facing new problems and challenges. Increases in network complexity and information volume make resources and network control more and more difficult. We argue that in such an unpredictable, changing and open environment, intelligent agents give the opportunity to obtain an optimized network management and control. In fact, the main features of agents, namely the autonomy, the ability to communicate with the others, the ability to solve common problems in a decentralized manner, the ability to cooperate. In addition some learning aptitudes, should allow agents to operate in network dynamic environment.
 In this project, we are interested in adaptive management and control approaches based on agents which monitor the network state and control its different components. Our aim is to include some intelligent and dynamic control, thanks to agents, allowing us to guarantee a QoS and to give a better management and global performance of the network.

The monitoring that could be performed using intelligent agents has to be:

• scalable: the monitoring approach is scalable because it is based on a multi-agent system which scales well with the growing size of the monitored network. For that, one has to integrate an agent (or a group of agents) on the new node to be controlled and the monitoring of this node is realized;
• distributed: each agent is responsible for a local monitoring. There is no centralization of the information collected by the different agents, and the decisions the agent performs are in no way based on global parameters. This feature is very important as it avoids having bottlenecks around a central monitoring entity;
• adaptive: the agent adapts its actions depending on the monitored data and entities according to the incoming events and the vision of the current system state. The monitoring that could be adopted is adaptive because of the following: (1) the agent modifies the monitored parameters: the agent decides, at every moment, which parameters must be monitored and which ones are no longer important under the current conditions; (2) the agent adapts the current management mechanisms (MM) and the actions undertaken when a certain event occurs. The actions the monitoring process executes may become no longer valid and must therefore be replaced by other actions. These new actions are considered more suitable to the current observed state;
• cooperating: the agents are cooperating to reach a common goal. This property is pretty important in large scale telecommunication networks to be sure about a common convergence of the system.
• local: the agent monitors only local parameters. However, the agent can use information sent by its neighbours (from other nodes) to adapt the monitoring process;
• selective: the agent filters the received events and reacts only to those it recognizes.
Event classification is used to trigger the appropriate actions.
 The goal of the network is to mediate communications on behalf of the user in a proactive way. Many of our current applications are governed by the pull model in which the user requests information or initiates a communication stream (Web, e-mail etc). In this interactive mode of operation, the user is an active part and must initiate the process of information acquisition. We believe that a personal communication environment should behave according to the push model in which a source of data or the network infrastructure takes care of preparing communications and proposing them to the user. In this model, it is the source or the network infrastructure that proposes the information to the user in a similar way to the SIP protocol which invites the user to a communication session. Such a mode of operation is at the base of the future Embedded Internet: it takes care of preparing communications and proposing them to the user.
 The role of the multi-agent system is to mediate notification events according to the user preferences, discover required services, configure them, and compose into complex communication applications. When an appliance enters a given zone, it authenticates itself so that the user preferences and device capabilities can be retrieved. Based on this information and the current context, the network infrastructure discovers relevant communication sources, component services, and applications that interact with the user. Then, it dynamically binds them to form a complete communication chain. The composition is guided by the information on user preferences, device capabilities, the current context, and a precise description of data types that can be generated on output or accepted on input by each component. The whole process is designed for minimal user intervention---we would like to automate most of the operations needed when appliances move and change network connections.

Task 1: Service and Resource Algorithms
 This task concerns the definition of algorithms that will have to control
- the allocation of flows in the physical network depending on user profile, SLAs (Service Level Agreements), and virtual networks characteristics.
- the distribution of the resources between the virtual networks.
 These algorithms have to be designed within the piloting plane. This task will define algorithms and protocols that will run in the piloting system. Those algorithms will export configuration parameters, which will allow the control plane to modify their behaviour on the fly.

 Task 2: Piloting system
 This Task will build the piloting system using the multi-agent environment and the Behaviours (and rule engine) proposed in WP 1. This distributed intelligent agent platform form the basis for the Piloting system.
 The structure of the agents was shown in Figure 3. This task will have to define the dynamic planner in details and the Behaviours dedicated to the decision process that can be use the intelligent Behaviour defined in WP1. The result of the work performs in the piloting plane is to feed the control algorithms defined in Task 1 with the best available information coming from the knowledge treated by the different Behaviours.

 Task 3: Situated View
 Based on the results that we will be obtained using the piloting system we will have to determine what are the best situated views for the different algorithms defined in Task 1 of this Work Package. Indeed, the situated view can be definitely different depending on the algorithm to be fed. For example, a one hop or a two-hop or a complete situated view can be defined but also more complex situated views as defined in Figure 10.

Fig. 10. Different solutions for the situated view (entire, intermediate and one-hop)

 We will have also to identify the frequency of updating each piece of information based on performance andQoS metrics. of the tk Deadline Leader

Research areas