Distributed Problem Solving Based on Recurrent Neural Networks Applied to Computer Network Management Analúcia Schiaffino Morales De Franceschi analucia@gpeb.ufsc.br Jorge M. Barreto* barreto@inf.ufsc.br Department of Electrical Engineering, Biomedical Engineering Research Group *Informatics and Statistics Department UNIVERSIDADE FEDERAL DE SANTA CATARINA PO. Box 476 Trindade 88.063-110 Phone: +55-48-3319422 Fax: +55-48-3319495 Florianópolis- Santa Catarina - Brazil Abstract With the application of new techniques, such as autonomous agents, artificial neural networks (ANN) and evolutionary computation, new questions arrive and may be, the most important areis: "can new problems be solved?" and "with how much effort?" This is particularly important when neural networks are used, since where a new computer paradigm is involved and a connectionist computability and complexity theory are still missing. To attach this problem we are developing some software autonomous agents based on recurrent neural networks. Observing the emergent behavior of ANN autonomous agents, and considering the initial results on complexity connectionist theory, a neural network for computer network management is developed.and presented 1 Introduction Application of new techniques to complex problem solving such as autonomous agents, artificial neural networks (ANN) and evolutionary computation have been growing up. With these new tools arrives new questionsnew question arrives and, the most important is: "can new problems be solved? with how much effort?" This is particularly important with neural networks where a new computer paradigm is involved and the construction of a connectionist computability and complexity theory must be accomplished. Neural computability was treated initially by McCulloch & Pitts using logic [1]. They proved the equivalence of a neural network with input devices and a Turing Machine. After, Arbib proposed an
intuitive demonstration of this equivalence [2]. However, in the complexity field, the two approaches are different because they require different resources. Minsky and Papert [3] provided the first contribution to such theory when they proved that a feed forward ANN must have a hidden layer o solve a non-linearly separable problem. Another result is that to solve a dynamical problem a recurrent dynamical ANN is simpler than a feed forward ANN one [4]. In the scientific literature, it is usual to find dynamic problems solved by static feed forward neural networks. In some formusing this approach, explicitly or implicitly the state of the dynamical system must be supplied, leading to a very big neural network and a corresponding longer training time even if the famous back propagation is used. To obtain a dynamical neural network it is possible: To apply a sequential line of time delays between each two inputs of a feed forward neural network; Using a network with cycles and dynamical neurons (ex: Hopfield network and recuurent neural networks). This work outlines the employment use of recurrent neural networks as distributed problem solving of the computer network management. It is organized in fivesix sections, the first beeing this one. The second presents the problem and approach which is based on application of the dynamic system to solve a distributed problem. The third section illustrates a simple example about recurrent neural network usage, and demonstrates why we should use dynamic systems. A distributed problem is outlined in four section four. And finally, following the conclusion and the respective references. 2 Recurrent Neural Networks Recurrent neural networks had been characterized by cycles, they have one or more in theiryour topology. They aremay be built with dynamical neurons and or have a sequential line of delays in the recursive conexion between the neuronsin one of layers. These delay elements are denoted in the text by z -1. The dynamical character feature of these topology permits the state representation and may be used in voice processing, industrial control and adaptive signal processing. Analysis of this topology allows to conclude that: It is applicable to computer network management because the network systems must be treated as dynamics systems. If a static neural network was used in a network system, then this neural network can not be used if the topology of this network was changed. The network management claims skill personnel, able to detect, diagnose and correct problems quickly and accurately, preferably before they affect the user community [5]. A recurrent neural network is able to implement this functions accurately and quickly after the training. Acho que podia botar uma figura de uma rede neural recorrente 3 Example 1 Parity Agent To attach? this problem we are developing some software autonomous agents based on recurrent neural networks [6]. The prototype was designed using JAVA and C language. Following two examples of these
agents. The first is a simple parity agent which serves as a didactic example and the second an interpreter network event agent which is a distributed version of an autonomous agent. To illustrate the fact that a recursive network can solve a dynamical problem with less neurons than a feed forward network let us Suppose suppose the 7-bit parity problem. It can be solved although a static feed forward network (Figure 1-B). However this solution is valid only for 7 bits. Another and more efficient approach is to use a recurrent neural network able to learn the parity concept (Figure 1-A) [7]. The recurrent neural network was trained using the synaptic weights from the feed forward 7-bit parity network,. And the start state was considered zero. Once trained, this neural network can solve the parity for any bit long string. Z -1 A A recurrent neural network to solve the n-bit parity problem. B - A classical feed forward neural network to solve a 7-bit parity problem. Figure 1 - Both neural architectures to implement a parity problem. 4 Example 2 - Distributed Solving Problem Dynamic, noisy and non stationary character of computer networks makes it hard to define what a fault is in a network environment. Diagnosis is the identification of a condition by its signs, symptoms or distinguishing characteristics [5]. Consider that the hosts of our test environment are sufficiently sophisticated to report network events. To do so, an autonomous agent has been developed to classify the network event as a Critical, Simple Failure, or No Failure. The agent is an interpreter of network events and would inform you when a problem is detected, by logging network events or by polling. Finding a fault, the agent must act or at least generate an alert to the user. INTERPRETER NETWORK EVENT Polling entry (PE) Event entry (EE) z -1 No Failure (NF) Simple Failure (SF) Critical Failure (CF)
Figure 2 - Recurrent neural network to implement the interpreter agent. The network was trained with the pattern shown in (Table 1) and the network implemented as illustrated in Figure 2. If was setting the polling and event entry then the system will classify as a critical failure then alert user. A simple failure will be characterized when one of the entries was setting. And classify as no failure when the system was reset. Table 1 - The pattern used to train the neural network. PE EE NF SF CF 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 1 1 0 0 1 To distributed problem solutions we are using the networking features of JAVA language, and this example will receive the signals from a ping command. This command needs to know about dropped to determine how good or bad the connection is. This may be interpreted by using the recurrent network above. Talvez desse pra desenvolver mais esta parte de solução distribuida (eu não entendi muito bem). 5 Conclusion The use of artificial intelligence is justified through the growing of the networks and the necessity of reliable services. A quick cost-benefit survey shows the following advantages [8]: Better quality of service: with the dissemination of the specialist throughout all segments of the network. The administrator's task is facilitated, providing a better performance; Greater agility, lower costs and greater productivity in the execution of services permitted by automation; Higher reliability, with decreased decision-making time; Training support for improved human resources preparation. In this sense, this work analyses the following questions: What kind of ANN must be used to solve the fault or performance management problem?; or, How rich must the hidden layer be to solve a distributed problem?; and, How may we construct a proactive network management using recurrent neural networks?. Koch [9], utilized autonomous agents based on ANN (classical feed forward with trained by back propagation) applied to management of computer networks. And iin 1997 a prototype employing artificial intelligence techniques to proactive network management was developed using the symbolic paradigm which were designed by observing the Ethernet network behavior [10][11][12][13]. In fact, these work
applied a dynamic character to a management network system using a recurrent neural networks. The future works are concentrated in develop another applications as mobile and telecommunications agents based in this theory. 6 References [1] McCulloch, W. S. and W. H. Pitts. A Logical Calculus of Ideas Immanent in Nervous Activity, Bull. of Mathematical Biophysics, vol.5, pp.115-133, 1943. [2] Arbib, M. A. Brains, Machines and Mathematics, McGraw-Hill,1964. [3] M. L. Minsky, S. A. Papert, "Perceptrons: an introduction to computational geometry", MIT Press, 1988 [4] M. Roisenberg, J.M.Barreto, F.M. de Azevedo. "A Neural Network that Implements Reactive Behavior Autonomous Agents". In: IASTED International Conference Artificial Intelligence, Expert Systems and Neural Networks. Honolulu, Hawaii, August 19-21, 1996. Pp. 245-248. [5] Maxion, R.A., Feather, F.E. A Case Study of Ethernet Anomalies in a Distributed Computing Environment, IEEE Transactions on Reliability, Vol 39, no. 4, oct., 1990. [6] M. Roisenberg, J.M.Barreto, F.M. de Azevedo, L.M.Brasil. "On a Formal Concept of Autonomous Agents". In: 16 th IASTED International Conference Applied Informatics, Germany, February, 1998. [7] J.M.Barreto, M.Roisenberg, F.M. de Azevedo. "Developing Artificial Neural Networks for Autonomous Agents Using Evolutionary Programming". In: IASTED International Conference Artificial Intelligence and Soft Computing. Cancun, May, 27-30, 1998. Pp. 283-286. [8] A.S.M. De Franceschi, M.A. da Rocha, H.L. Weber, C.B. Westphall, "Employing Remote Monitoring and Artificial Intelligence Techniques to Develop the Proactive Network Management", In IEEE International Workshop on Application of Neural Networks in Telecommunications, Melbourne, 1997. pp.116-123. [9] F. Koch Autonomous Agents for Computer Network Management, M. Sc. Dissertation, Federal University of Santa Catarina, Florianópolis, 1997. [10] A.S.M. De Franceschi, M.A. da Rocha, H.L. Weber, C.B. Westphall, "Proactive Network Management Using Remote Monitoring and Artificial Intelligence Techniques", In IEEE International Symposium on Computer Communications, Alexandria, 1997. [11] A.S.M. De Franceschi, L.F. Kormann, C.B. Westphall, "Performance Evaluation for Proactive Network Management", in IEEE/ICC 96 International on Communications Conference, Vol. I, Dallas, Texas, Jun., 1996. [12] A.S.M. De Franceschi, L.F. Kormann, C.B. Westphall, "A Performance Application for Proactive Network Management", in Second IEEE International Workshop on Management Systems, Toronto, Jun., 1996. Pp. 15-20. [13] A.S.M. De Franceschi. "A Performance Application for Proactive Network Management", M. Sc. Dissertation, Federal University of Santa Catarina, Florianópolis, 1996.