Intelligent, Dynamic and Distributed Solutions for Network Management A.S.M. De Franceschi*, J.M. Barreto**, M. Roisenberg** Federal University of Santa Catarina * Electrical Engineering Graduate Program - Biomedical Engineering Research Group ** Computer Science Graduate Course – Informatic and Statistic Department P.O. Box 476 – Trindade – 88.040-900 Florianópolis- Santa Catarina - Brazil E-mails:
[email protected],
[email protected] [email protected]
Abstract: This work presents an alternative to the centralized approach of computer network management. In network management platforms, we usually find passive managers and the agents who just have the function of collecting data samples composing the environment. They have no autonomy and the network administrator must reach a decision. This work investigates what is the most suitable Artificial Intelligence (AI) technique applicable in the distributed problem solving of computer network management. In addition, it is usual to find in scientific literature, dynamic problems solved by static feedforward neural networks. Using this static 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 backpropagation is used. The AI techniques will be implemented in autonomous agent format which will be able to automate the network management process. Keywords: Artificial Intelligence, Neural Networks, Autonomous Agents, Computer Network Management, Distributed Management, Dynamic Systems.
1. Introduction Application of new techniques to complex problem solving, such as, autonomous agents, artificial neural networks (ANN) and evolutionary computation has been developing. With these new tools arrive new questions and the most important is: "Can new problems be solved? and,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 initially treated by McCulloch & Pitts (1943) using logic. They proved the equivalence of a neural network with input devices and a Turing Machine. Later, Arbib (1964) proposed an intuitive demonstration of this equivalence. However, in the complexity field, the two approaches are different because they require different resources. Minsky and Papert (1988) provided the first contribution to such theory when they proved that a feed forward ANN must have a hidden layer to solve a non-linearly separable problem. Another result is that to solve a dynamic problem, a recurrent dynamic ANN is simpler than a feed forward one (Barreto 1996), (Roisenberg et al. 1996).
The proposal of this work is to apply intelligent and dynamic tools and techniques to develop autonomous agents as a distributed solution for network management. At the same time, we will study distributed problem solving to accomplish the connectionist computability and complexity theory mentioned above. So, this work presents an alternative to the centralized approach of computer network management (Goldzsmith and Yemini 1993, Silva and da Silva 1999). In the network management platforms, we usually find passive managers and the agents who just have the function of collecting data samples composing the environment. They have no autonomy and the network administrator must reach the decisions. This work investigates the most suitable Artificial Intelligence (AI) technique applicable in the distributed problem solving of computer network management. In addition, it is usual to find in scientific literature, dynamic problems solved by static feedforward neural networks. Using this static approach, explicitly or implicitly the state of the dynamic system must be supplied, leading to a very big neural network and a correspondingly longer training time even if the famous backpropagation is used (De Franceschi and Barreto 1999). The AI techniques will be implemented in autonomous agent format (Roisenberg et al. 1998) which will be able to automate the network management process.
N etwork M an ag em en t D istrb u ted P rob lem
R eactive S olu tion
P roac tive S olu tion
A u ton om ou s A g en ts w ith S tatic B eh avior
A u ton om ou s A g en ts w ith D yn am ic B eh avior
F eed forw ard A rtific ial N eu ral N etwork (F A N N )
R u les P rod u ction (R P )
D yn am ic A N N
A p p lyin g a seq u en tial lin e b etween FA N N in p u ts
U sin g A N N w ith cycles or d yn am ic n eu ron s
R ec u rren t N N Figure 1: Methodology Application.
H op field N N
2. Methodology Application Analyzing the network management process as a whole, we have a big complex problem. However, using the functionality division of the OSI reference model produces five smaller problems. It was investigated whether these problems have static or dynamic features. In addition, the network management behavior was also considered, i.e., whether was reactive or proactive. The following methodology (Figure 1) is defended in the present work. If it is a static problem then it must have a heuristic solution. There are two popular forms to implement this case: through production rules (Symbolic paradigm) or using feedforward neural networks (Connectionist paradigm). On the contrary, the dynamic problems must be “well” solved by dynamic tools. To obtain dynamism in a neural network solution it is possible to apply a sequential line of time delays between two inputs of a feedforward neural network, or to use a network with cycles and dynamical neurons (ex: Hopfield network and recurrent neural networks). Network Status
ACTIVE
INACTIVE
Proactive Management
Reactive Management
Network Degradation Figure 2: Reactive and Proactive Management.
3. Reactive solutions Maybe the most famous reactive solution is the diagnostic system. The symptoms are supplied as the input to the system and as an output, we have the problem diagnosis (De Franceschi et al. 1997). The actions for problem solving are taken only after the network degradation (Figure 2). This is the biggest disadvantage of this kind of solution. The diagnostic systems are normally used in the Fault Management area. Some faults are impossible to prevent, such as, the actions of the environment above the cables or bad equipment quality. In addition, the Accounting Management area has static features because it has no state changes.
4. Autonomous Agents with Static Behavior As mentioned, we have two distinct attitudes to approaches problem solving. First, we have the autonomous agents with static features. The most famous tools to construct this behavior are production rules and feedforward neural networks. The production rules are a
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declarative solution constructed with the words “if” and “then”. Using feedforward neural networks, the input and output patterns are known. In this case, the network is trained to learn the patterns and it has no state changes.
5. Feedforward ANN and Production Rules There are two forms on which to model the heuristics to solve static problems. The feedforward ANN are networks without cycles, and are normally composed of input, output and hidden layers. The Production Rules are the inference reasoning. Normally, problem solving is a sequence of steps. Therefore, the response set is a sequence of decisions (Barreto 1996). Considering the baseline is the network profile as a network performance database. Samples of the production rules follow (De Franceschi et al.1997): if ∆ input packets > baseline and output packets < baseline and output errors > baseline then “wrong packets”
(rule1)
if ∆ input packets > baseline and output packets < baseline and output errors > baseline and output packets queue > baseline then “wrong packets burst”. (rule 2) Meanwhile, this knowledge could be modeled using a feedforward ANN. In this case, we will have a similar network as the Figure 3. This network might be trained to classify the problem as the “wrong packets” or “wrong packets burst”.
Figure 3: Modeling the problem with Feedforward ANN.
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6. Proactive Solutions If the actions are taken before the network degradation, the solution is called proactive (Figure 2). The main goal of proactive management is detecting possible problems (in a network) before they happen and lose the network active state. Nevertheless, it is necessary to design a profile, baselining samples of resources behavior of the managed network. In this case, we have no heuristics about the problems that are normally status changing. For these solutions, we may construct autonomous agents with dynamic features.
7. Autonomous Agents with Dynamic Behavior Another case, is when the problem needs a dynamic solution we expect dynamic behavior from the agents. As mentioned earlier, to construct a dynamic solution we may apply a sequential line of time delays between the inputs of a feedforward neural network. Alternatively, we may use a network with cycles and dynamical neurons (ex: Hopfield network and recurrent neural networks (Roisenberg et al. 1998). In the present work, we have used recurrent neural networks to implement the solution through the examples (Figure 4).
8. Recurrent ANN When the ANN has cycles and dynamic neurons with delays they are called recurrent. With this neural network topology estimates the unknown state changes when the input and output patterns are known. The examples collected from computer networks provide the input and output patterns which are needed to construct the neural network. This model is based on recurrent neural networks which were proposed firstly by Roisenberg (Roisenberg et al. 1996) to construct autonomous agents.
Z -1
Figure 4: Recurrent ANN.
9. Conclusion The network management claims skilled personnel, are able to detect, diagnose and correct problems quickly and accurately, preferably before they affect the user community (Maxion and Feather 1990). With the correct employment of the AI techniques, as feedforward and
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recurrent neural networks, we are able to implement these functions (in Autonomous Agents format) accurately and quickly after the training. In this sense, this work analyses the following questions: What kind of management behavior should we use, proactive or reactive? The behavior should be proactive in cases where we have the ability to prevent the actions, by acting to improve the network performance for instance. On the other hand, when we have heuristics in the problem (after it has happened), the management should be called reactive, as a diagnostic process. What kind of ANN must be used to solve the fault or performance management problem? In the fault management area, firstly we have analyzed what kind of management behavior we will adopt. Secondly, we will analyze if the management has dynamic or static features. In the performance management area, we should adopt a proactive management and should implement autonomous agents with dynamic features. How rich must the hidden layer be to solve a distributed problem? The question of the hidden layer was solved by Minsky and Papert (1988) when they proved that a feedforward ANN must have a hidden layer to solve a non-linearly separable problem. For distributed problems, we also have non-linearly separable problems, but we can’t estimate accurately how many neurons the hidden layer should have. How may we construct a proactive network management using recurrent neural networks? Using a formal concept based on Systems Theory proposed by Roisenberg (1996). This modeling consists of finding a Finite Status Machine from samples of the problem. If the problem has a finite input and output set, we can estimate the status changes from a recurrent neural network. 10. References Arbib MA (1964) Brains, Machines and Mathematics. McGraw-Hill De Franceschi ASM, Barreto JM (1999) Distributed Problem Solving Based on Recurrent Neural Networks Applied to Computer Network Management. Proceedings of International Conference on Telecommunications (2): 323-327 De Franceschi ASM, da Rocha MA, Weber HL, Westphall CB (1997) Employing Remote Monitoring and Artificial Intelligence Techniques to Develop the Proactive Network Management. Lawrence Erlbaum Associates Publishers: 116-123 Goldzsmith G, Yemini Y (1993) Evaluation Management Decisions via Delegation. Proceedings of IFIP International Symposium on Integrated Network Management Barreto JM (1996) Connectionism and Problems Resolution (in portuguese). Titular professor contest dissertation, Federal University of Santa Catarina, Informatic and Statistic Department Maxion RA, Feather FE (1990) A Case Study of Ethernet Anomalies in a Distributed Computing Environment. IEEE Transactions on Reliability 39 (4) McCulloch WS, Pitts WH (1943) A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5: 115-133 Minsky ML, Papert SA (1988) Perceptrons: an introduction to computational geometry. MIT Press Roisenberg M, Barreto JM, de Azevedo FM (1996) A Neural Network that Implements Reactive Behavior Autonomous Agents. Proceedings of International Conference on Artificial Intelligence, Expert Systems and Neural Networks : 245-248 Roisenberg M, Barreto JM, de Azevedo FM, Brazil LM (1998) On a Formal Concept of Autonomous Agents. Proceedings of International Conference Applied Informatics
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Silva JL de C da Silva AN, de Souza JN (1999) Applying Cooperative Remote Objects and Mobile Agents Concepts for Management by Delegation in Heterogeneous Network Systems. Proceedings of International Conference on Telecommunications (2): 313-318
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