ATM network management based on a distributed artificial intelligence architecture
1
José-Luis Marzo
Pere Vilà
Ramón Fabregat
IIiA1. Universitat de Girona Avda Lluís Santaló s/n E-17071 Girona (Spain) +34 972 41 84 97
IIiA. Universitat de Girona Avda Lluís Santaló s/n E-17071 Girona (Spain) +34 972 41 84 75
IIiA. Universitat de Girona Avda Lluís Santaló s/n E-17071 Girona (Spain) +34 972 41 84 75
[email protected]
[email protected]
[email protected]
ABSTRACT
2. DEFINITIONS AND PROBLEM SPECIFICATION
In this paper, we describe a multi-agent architecture for Virtual Path (VP) management (i.e. bandwidth and restoration) in Asynchronous Transfer Mode (ATM) networks. The intention is not to propose new strategies, but to improve the management integrating these different management mechanisms by using distributed artificial intelligence. Major objectives are scalability and robustness of the system. Our approach uses two different multi-agent systems. The first one consists of simple and small reactive agents that control and monitor the network elements (nodes and virtual path). The second one is another multi-agent system of more complex agents that monitors and controls the previous ones, while it is also allowed to change the allocated resources.
ATM networks [1] have VP Network several layers of hierarchy. One of these layers is the Virtual Path layer, which is Virtual Paths used to simplify the establishment of new connections and also Physical Network constitutes a virtual topology over the physical network Figure 1 Virtual Path (see figure 1). This allows Network dynamically managing this virtual topology and adapting it to improve the network resource utilisation. The two main VP management functions, which our approach aims to deal with are described below.
Keywords Network Agents, Multi-agent Systems, Dynamic Bandwidth Management, Fault Management, Asynchronous Transfer Mode.
2.1 Bandwidth Management Bandwidth management attempts to manage the capacities assigned to the set of VPs. When some VPs become congested, then some connections that could be accepted if the traffic load were better balanced may be rejected. Two actions are usually taken by the bandwidth management system (figure 2).
1. INTRODUCTION In current communications networks, the available management tools and frameworks are based on a centralised point of view and consequently do not scale well. They also make use of too much human network manager involvement on the day-by-day operations. This work proposes the use of intelligent agents to automate these management functions and improve the network performance. Those agents are defined as software entities with special properties (autonomy, social ability, reactivity and proactiveness) [2][4].
VP 2
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VP 1
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VP 1 - congested VP 2 - under utilised VP 2 VP 1
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This work is initially oriented to dynamic bandwidth allocation to virtual path in the context of ATM networks. Moreover, same principles can be applied to networks where the allocation resource concept is implemented.
Bandwidth re-allocation
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VP 1
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VP 1 - congested VP 2 - near congestion
VP re-routing
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Figure 2 Bandwidth Management
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Institut d’Informàtica i Aplicacions (IIiA) This work is partially supported by the CICYT (Spanish Education Ministry, under contract TEC-98-0408-C02-01)
2.2 Fault Management – VP Restoration Schemes It is very important that today’s networks should be fault-tolerant. Rapid restoration after a failure is required to achieve that. The ultimate goal is that the customers do not perceive the failures. There are two types of restoration schemes: dynamic and preplanned. Pre-planned schemes (figure 3) are based on preassigned backup VP whereas dynamic schemes are based on flooding algorithms and search for restoration routes by broadcasting messages after the failure is detected. Pre-planned schemes achieve effective and rapid restoration, but more spare resources are needed. Backup VP (zero bandwidth) Bandwidth capture message
Link Switch Node
Active VP
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Fault
Figure 3: Pre-planned restoration Hybrid restoration is when both schemes are applied; i.e. some priority VPs can be protected with pre-planned schemes and others, of less priority, with dynamic schemes.
3. MULTI-AGENT APPROACH We propose an ATM VP management system based on intelligent software agents. The main characteristics of the system are its scalability, robustness and simplicity. Our goals are to maximise the autonomy of the agents and minimise the communications between them. Preliminary details on the present proposal can be obtained in [3]. This approach is based on two different multi-agent systems (MAS) which are independent of each other. They are called Network Monitoring MAS (NM-MAS) and Network Planning MAS (NP-MAS). Robustness implies a certain duplication of resources, i.e. more than one agent monitors a VP, watching, for example, for the possibility of node failure. Scalability means that the number of agents does not increase exponentially when the network grows but also means minimising the network traffic; i.e. the communications between agents. An interesting restriction is that the agents situated in the nodes can only communicate with other agents in the same node and with their neighbours.
3.1 Network monitoring The main functions of Network Monitoring-Multi Agent System (NM-MAS) agents are to monitor and to control the network. The main goal is that they are simple and react fast when an event (connection attempts, connection releases, load changes, faults, etc.) occurs. To achieve these goals, a pure reactive structure is proposed [4]. These agents act by using a stimulus/response behaviour to respond to the state of the environment in which they are embedded. Therefore, a simple architecture based on rules is proposed for this kind of agent. As we want to implement VP networks with different levels of priority, the NM-MAS agents are also grouped at different levels of priority (figure 4 shows the NM agents named MX).
Each pair of these agents PA monitors and controls one VP. The Physical Link (PL) is monitored in the same VP5 way. They are situated in the M5 end points of the VP or PL and have to know everything VP4 about it, e.g. the number and VP2 characteristics of the VC M3 going through a VP, the VP1 M1 M2 connection attempts, if the VP is under- or overD C utilised, etc. This information is stored as B MAD knowledge and statistics, not MAB A as bulk data, and is used for the NP-MAS system. We Figure 4: NM and NP assume that the high priority agents in one node VPs are protected using preplanned restoration and the NM-MAS agents are put in charge of this task. As they are reactive agents they have a fast response and when a fault occurs they can activate the backup VP, which is also controlled by the same pair of agents that controls the original VP.
3.2 Network Planning The mission of these agents (NP-MAS) is to monitor and control the whole network through monitoring the NM-MAS agents, instead of monitoring directly the network. These agents consult the NM-MAS agents’ knowledge and modify their rules and goals by including the NP-MAS information. Another task of this system is to maintain a distributed overall view of the network through the communication of each agent with its neighbours (the scalability restriction). There is only one NP-MAS agent in each node (see figure 4, agent PA). These agents must be bigger than the first ones and have reasoning and planning subsystems. Another task of this system is the creation and release of VPs and this implies the creation and destruction of NM-MAS agents. This system also assumes dynamic VP restoration, making use of a negotiation mechanism with its neighbours. This means that more powerful architecture is required for the NP-MAS agents. As their goal is to maintain a distributed overall view of the network in order to make plans and forecasts, its work is collaborative. This means that each agent possesses limited domains, information or resources, but that, by pooling together their abilities, agents are able to solve problems beyond the capacity of any one single agent.
4. REFERENCES [1] Kyas O. “ATM Networks”, International Thomson Computer Press 1995, ISBN 1-85032-128-0
[2] Nwana H.S., “Software Agents: An Overview”, Knowledge Engineering Review, 11, 1996
[3] Vila P., Marzo J.L., Fabregat R, Harle D. "A multi-agent Approach to Dynamic Virtual Path Management in ATM Networks. IMPACT'99 Workshop. Seattle December 1999.
[4] Wooldridge M., Nicholas R. Jennings, “Intelligent Agents: Theory and Practice”, Knowledge Engineering Review, January 1995
