tors activity includes the supervision and monitoring of network services (ap- ... As is shown in Figure 1 the managed network nodes provide information to.
Memorias del 7mo. Congreso Internacional en Ciencias Computacionales, CiComp 2014
Network Management based on Multi-Agent Architecture Karina Raya-D´ıaz, Carelia Gaxiola-Pacheco and Manuel Casta˜ n´on-Puga Autonomous University of Baja California Chemistry and Engineering Department Calz. Universidad 14418, Tijuana, BC, M´exico, 22390 {karina.raya, cgaxiola, puga}@uabc.edu.mx
Abstract. This paper has the aim to propose a network management architecture based on intelligent agents. The approach of this architecture is the autonomous management of the network by design a collaborating method based in task-oriented domain. This method has to implement a negotiation mechanism in the agents to improve the performance of the network flows. Key words: Architecture, Agents, Management, Network, Task-Oriented
1
Introduction
The network management domain at operational level has not evolved as much as the applications for networks devices. A traditional IT administrator has to monitoring of technical failures and solves it manually. Actually IT Administrators activity includes the supervision and monitoring of network services (applications) through bandwidth management, traffic analysis, application functionality, identifying bottlenecks and more. To achieve these activities, the IT administrator must have network management software and hardware that enable him to perform transparently assessing the quality of service parameters that influence the performance of the network. Promising developments in this domain are the multi-agent architectures that integrate a negotiation mechanism to optimize resources allocation in policy-based networks [1],[2],[3],[4].
2
Network Management
Network Management is defined as planning, organizing, monitoring and controlling communications elements to ensure an adequate level of service, and according to a particular cost [1],[2]. Network management has a functional model that describes five areas: 1. Fault: Functions that allow the detection, notification, isolation and correction of anomalies within the system.
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2. Configuration: Procedures to control, identify, collect and deliver data management objects. 3. Performance: Functions that measure the evolution of a system through statistical objects. 4. Accounting: Features that account for the use of certain resources. 5. Security: Functions related to security control access to different resources. To carry out the implementation of management within a network being requires a computing platform which integrates applications to adapt to the complex and changing environment.Traditional client-server management network architectures are wasteful in their usage of bandwidth[5]. 2.1
Management Based on Intelligent Agents
Intelligent agents are autonomous software entities able to store knowledge about themselves and their environment [2],[6]. The agents have intelligence implemented by a method used to develop their logic or reasoning, they also could communicate and cooperate with each other. Collaboration is an attribute that allows intelligent agent to interact or negotiate with other entities of agents, and these negotiations could be based on an artificial intelligence method.
Fig. 1. Components of Management System
The manager-agent paradigm is a concept that applies in most network management systems which exchange information among management nodes and agents that manage them [1],[7]. The system has two essential elements:
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– Manager: Is a standalone device which can translate the requirements of the network administrator in monitoring and control messages to remote devices. The collected information be stored in a Database of Management Information. The manager acts as translator between human network administrator and the network management system. – Agent: Responds to requests for information and perform actions in the managed nodes. As is shown in Figure 1 the managed network nodes provide information to the managing agents, they react based on the parameters defined in the policies acting on the network with control messages over the nodes, optimizing network performance through configuration changes [1][8].
2.2
Applications Domains of Multi-Agent System
The following are some examples of the application domains of multi-agent systems [9] :
– Evaluation of distributed situations: With emphasis on how agents with different areas of awareness and control, can share their local interpretations in order to respond to a situation, that is, generate a diagnostic computer. – Scheduling and resource allocation planning: This type of system is emphasized in how agents can be coordinated to avoid conflicts over resources of a system; examples are scheduling tasks in factories, management networks and intelligent environments. – Experts Distributed Systems: Whereas officials to share information and negotiate design solutions together, this type of system requires information from experts in the area express criteria. – Agents Based on Distributed Artificial Intelligence: In the context of multi-agent systems, the agents could be based on distributed artificial intelligence the aim of these types of systems is coordinate a set of autonomous agents. Artificial intelligence proposes methods for coordination between agents, which must consider the objectives and utilities of each agent together to establish what actions to take to provide a solution to a given problem. Considering the above features and applications, intelligent agents are the suitable tool for self-management within the area of telecommunications, specifically the autonomous networks [10]. Autonomous networks have intelligent nodes (through agents) which detect themselves changes in the parameters of quality of service; these agents isolate or anticipate failures taking decisions to manage the network so it will be proactive.
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3
Propose Multi-Agent Architecture
Network management based on multi-agents systems have been a popular approach to reach this complex task. Computational modelling such as multi-agent system could help to understand the interactions and behaviors of the network [10],[11],[12]. The agents in this system have to deal situations of collaboration and competition to implement the policies that govern the network.
Fig. 2. Multi-Agent Architecture
The proposed hierarchical multi-agent architecture is presented in the Figure 2. The network administrator spicifies and mantain the high-level policies. At the lower level, there is a set of agents that collect data related to QoS parameters by sensing the current state of the network. These agents transfer the data to the level that holds the decision-making module; through a negotiation mechanism between a set of collaborative agents that are govern by the network policies [13], [4]. As a result of the negotiation the actions to be performing by the agents of the low-level actuators are determined, this will result in the optimization of network. 3.1
Formalized Description
Complex problems as network management could be solved by converting the problem into another problem in such a way that a solution to the second problem can be used to solve the first problem, this method is called reducibility [14].
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Assuming this method we represent a Management Network problem as a finite set of of tasks marked as T.
The proposed multi-agent architecture, shown in Figure 2, specifies two types of agents: – Sensing Agents (As): Are model-based reflex agents which store information about the current state of the world using an internal model; the action performed by an agent is based on its perception and rules [6]. A sensing agent ıAs is defined by a tuple where S is the actual state of the parameter that is monitoring, R is a set of rules and T is set of the possible task to execute. – Collaborating Agents (Ac): Are utility-based agents those use a model of the world and an utility function which will allow the rational decision making performed by every agent. [6]. A collaborating agent ıAc is defined by a tuple , where S is the state of world, T is the set of possible tasks, P are the policies or rules, C is the cost of a deal δ, finally there is a utility U for deal δ, calculate by the cost of execute Ti minus the cost of accept the deal δ. The task-oriented domain is a way to use Artificial Intelligent methods to implement a negotiation protocol because the agents have a function that rates the utility of each deal, so they can make a decision and optimize its current utility [15].
4
Conclusions
The negotiation mechanisms in multi-agent architectures should ensure the efficiency of the decision-making module. Context aware is an essential feature in all agents in the architecture. Policies are the rules to optimize the performance of the network, and the parameters to establish a decision function that governs the behavior of collaborating agents.
Acknowledgements K. Raya gratefully acknowledges the scholarship from CONACyT to pursue her Ph.D. studies and C. Gaxiola wish to thank UABC for financial support at the research project Framework para el desarrollo de agentes h´ıbrido- inteligentes con conciencia de la situaci´ on”.
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