Multiagent Systems

Agent-based technology offers a solution to the problem of designing efficient and flexible network management strategies. The OMG has produced the MASIF, which focuses on mobile agent (object) technology, in particular, allowing for the transfer of agents code and state between heterogeneous agent platforms.

The Intelligent Network (IN) was developed to introduce, control, and manage services rapidly, cost effectively, and in a manner not dependent on equipment and software from particular equipment manufactures. The architecture of an IN consists of the following node types: Service Switching Points (SSPs), Service Control Points (SCPs), Service Data Points (SDPs), and Intelligent Peripherals (IP). These nodes communicate with each other by using a Signaling System No. 7 (SS7) network. SSPs facilitate end user access to services by using trigger points for detection of service access codes. SCPs form the core of the architecture; they receive service requests from SSPs and execute the service logic. SCPs are assisted by SDPs, which store service/customer related data, and by IPs, which provide services for interaction with end users (e.g., automated announcements or data collection).

IN overloads occur when the load offered to one or more network resources (e.g., SCP processors) exceeds the resource's maximum capacity. Because of the central role played by the SCP, the overall goal of most IN load control mechanisms is to protect SCP processors from overload. The goal is to provide customers with high service availability and acceptable network response times, even during periods of high network loading. Load control mechanisms are designed to be

• efficient - keeping SCP utilization high at all times;

• scalable - suited to all networks, regardless of their size and topology;

• responsive - reacting quickly to changes in the network or offered traffic levels;

• fair - distributing system capacity among network users and service providers in a manner deemed fair by the network operator;

• stable - avoiding fluctuations or oscillations in resources utilization;

• simple - in terms of ease of implementation.

The majority of IN load control mechanisms are node-based, focusing on protecting individual nodes in the network (typically SCPs) from overload. Jennings et al. argue that node-based mechanisms cannot alone guarantee that desired Quality of Service (QoS) levels are consistently achieved. The following observations support this viewpoint:

• Most currently deployed node-based mechanisms were designed for standard telephony traffic patterns. Present and future INs support a large number of heterogeneous services, each exhibiting changing traffic characteristics that cannot be effectively controlled by using node-based techniques.

• Existing node-based overload protection mechanisms serve to protect individual nodes only and may cause the propagation of traffic congestion, resulting in adverse effects on the service completion rates of the network as a whole.

• Typically node-based mechanisms do not interact effectively with the protection mechanisms that are incorporated into the signaling networks that carry information between the nodes in a network.

• Node-based controls typically focus on SCP protection only.

• Telecommunications equipment manufactures implement node-based mechanisms on a proprietary basis. This can lead to difficulties in effectively controlling traffic in INs that contain heterogeneous types of equipment.

While flexible and adaptable network-based load control mechanisms can be implemented by using standard software engineering techniques, Jennings et al. argue that there are many advantages of adopting an agent-based approach:

• Methodology: The agent paradigm encourages an information-centered approach to application development; thus it provides a useful methodology for the development of control mechanisms that require manipulation of large amounts of data collected throughout the network.

• Agent communication languages: Advanced communication languages allow agents to negotiate in advance the semantics of future communications. This is not present in traditional communications protocols and can be used in mechanisms that adapt to dynamic network environments in which, for instance, traffic patterns change as a result of the introduction or withdrawal of services.

• Adaptivity: The agents adaptive behavior allows them to learn about the normal state of the network and better-judge their choice of future actions.

• Openness: Agents can exchange data and apply it in different ways to achieve a common goal. This means that equipment manufacturers can develop load control agents for their own equipment, but these agents can still communicate with agents residing in other equipment types.

• Scalability: The agent approach allows for increased scalability to larger networks. For instance, an agent associated with a recently introduced piece of equipment can easily incorporate itself into the agent community and learn from the other agents the range of parameters that it should use for its load control algorithm.

• Robustness: Agents typically communicate asynchronously with each other and thus are not dependent on the prompt delivery of interagent messages. The ability to act even during interrupted communications (e.g., due to overload or network failures) is a desirable attribute of a load control mechanism.

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