Autonomous Software Agents for Computer Network Management A. S. M. De Franceschi*, J. M. Barreto, M. Roisenberg
[email protected],
[email protected],
[email protected] Electrical Engineering Graduate Program, Biomedical Engineering Research Group* Computer Science Graduate Course UNIVERSIDADE FEDERAL DE SANTA CATARINA PO. Box 476 – Trindade – 88.040-900 Phone: +55-48-3319422 Fax: +55-48-3319495 Florianópolis- Santa Catarina – Brazil
1. Introduction This work presents an alternative for the centralized approach of computer network management [1][2]. In the network management platforms, we usually find passive managers and the agents who just have the function to collect data samples composing the environment. They have no autonomy and network administrator must reach the decision make. This work is investigating what is the most suitable Artificial Intelligence (AI) technique applicable in
distributed problem solving of computer network management. In addition, it is usual to find, in the 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 [3]. The AI techniques will be implemented as autonomous agents format [4] which will be able to automating the network management process.
N etwork M an ag em en t D istrb uted P rob lem
R eactive S olu tion
P roactive S olu tion
A uton om ou s A g ents w ith S tatic B ehavior
A uton om os A gen ts with D yn am ic B ehavior
Feed forward A rtificial N eural N etwork (FA N N )
R ules P rod uction (R P )
D yn am ic A N N
A p plyin g a sequ en tial line b etween FA N N in pu ts
U sin g A N N with cycles or dyn am ic neu ron s
R ecu rren t N N
Figure 1 – Methodology Application.
H opfield N N
2. Methodology Application Analyzing the network management process as a whole, we have a big complex problem. Moreover, using the functionality division of the OSI reference model it turns five smaller problems. These problems were investigated if they have static or dynamic features. In addition, the network management behavior also was considered, if it was reactive or proactive. The following methodology (Figure 1) is defended in the present work. If is a static problem then it must have a heuristic solution. There are two most popular
Network Status
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. There are following forms presently to include dynamism in a neural network solution. To apply a sequential line of time delays between two inputs of a feedforward neural network, or using a network with cycles and dynamical neurons (ex: Hopfield network and recurrent neural networks).
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 input of the system and as output, we have the problem diagnose [5]. The actions for problem solution are taken only after the network degradation (Figure 2). This is the most disadvantage of this kind of solution. The diagnostic systems are normally used in the Fault Management area. Some faults are impossible to prevent as the actions of the environment above the cables or the bad equipment quality. In addition, the Accounting Management area has static features because has no state changes. 4. Autonomous Agents with Static Behavior As mentioned, we will have two distinct behavior to distributed 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 declarative solution constructed with the words if and then [4]. 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 to model the heuristics to solve static problems. The feedforward ANN are networks without cycles, and normally are composed by input, output and hidden layers. The Production Rules are the inference reasoning. Normally, the problem solving is a sequence of steps. Therefore, the response set is a sequence of decisions [6]. Considering the baseline is the network profile as a network performance database. Samples of the production rules following [5]: if ∆ input packets > baseline and output packets < baseline and output errors > baseline 1 then “wrong packets”
(rule1) if ∆ input packets > baseline and output packets < baseline
1
Term used to indicate that some packets are not treated in time.
and output errors > baseline and output packets queue > baseline then “wrong packets burst”.
similar network as the Figure 3. This network might be trained to classify the problem as the “wrong packets” or “wrong packets burst”.
(rule 2) Meanwhile, this knowledge could be modeled using a feedforward ANN. In this case, we will have a
Figure 3 - Modeling the problem with Feedforward ANN.
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 [4]). In the present work, we had used recurrent neural networks to implement the solution through the examples (Figure 4).
6. Proactive solutions If the actions are taken before the network degradation, the solution is called proactive (Figure 2). The main goal of the proactive management is detecting possible problems (in a network) before they happen and lost the network active state. Nevertheless, it is necessary to design a profile, baselining samples of the resources behavior of the managed network. In this case, we have no heuristics about the problems that normally have status changing. For these solutions, we may construct autonomous agents with dynamic features.
8. Recurrent ANN When the ANN have cycles and dynamic neurons with delay 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 network provides the input and output patterns which are need to construct the neural network. This model based on recurrent neural networks was proposed firstly by Roisenberg [4] to construct autonomous agents.
7. Autonomous Agents with Dynamic Behavior Another case, when the problem needs a dynamic solution we have a dynamic behavior expected from the agents. As mentioned, to construct a dynamic solution we may apply a sequential line of time
Z -1
Figure 4 - Recurrent ANN.
9. Summary The network management claims skill personnel, able to detect, diagnose and correct problems quickly and accurately, preferably before they affect the user community [8]. With the correct employment of the AI techniques, as feedforward and recurrent neural network, we able to implement these function (as 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 that cases which we have the ability to prevent the actions, as acting to improve the network performance for instance. On the contrary, when we have heuristics about the problem (after they 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, first we have analyzed what kind the management behavior we will adopt. Second, 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 is to solve a distributed problem? The question of the hidden layer was solved by Minsky when they proved that a feedforward ANN must have a hidden layer to solve a non-linearly separable problem [8]. For distributed problems, we also have nonlinearly separable problems, but we may not accurate how many neurons should we have in the hidden layer. • How may we construct a proactive network management using recurrent neural networks? Using a formal concept based on Systems Theory proposed by Roisenberg [4]. This modeling consists in find a Finite Status Machine from samples of the problem. If the problem has finite input and output sets, we can estimate the status changes from a recurrent neural network.
[1]
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