Knowledge Discovery Using Bayesian Network Framework for ...

Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management Abul Bashar1 , Gerard Parr1, Sally McClean1 , Bryan Scotney1 , and Detlef Nauck2, 1

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School of Computing and Engineering, University of Ulster, Coleraine BT52 1SA, UK {bashar-a,gp.parr,si.mcclean,bw.scotney}@ulster.ac.uk Research and Technology, British Telecom, Adastral Park, Ipswich IP5 3RE, UK [email protected] Abstract. The ever-evolving nature of telecommunication networks has put enormous pressure on contemporary Network Management Systems (NMSs) to come up with improved functionalities for efficient monitoring, control and management. In such a context, the rapid deployments of Next Generation Networks (NGN) and their management requires intelligent, autonomic and resilient mechanisms to guarantee Quality of Service (QoS) to the end users and at the same time to maximize revenue for the service/network providers. We present a framework for evaluating a Bayesian Networks (BN) based Decision Support System (DSS) for assisting and improving the performance of a Simple Network Management Protocol (SNMP) based NMS. More specifically, we describe our methodology through a case study which implements the function of Call Admission Control (CAC) in a multi-class video conferencing service scenario. Simulation results are presented for a proof of concept, followed by a critical analysis of our proposed approach and its application. Keywords: Next Generation Networks (NGN), Network Management, Bayesian Networks (BN), Call Admission Control (CAC).

1

Introduction

The area of telecommunication networks is changing at a rapid pace in terms of its architecture and services due to the advances in the underlying technology and also owing to the new demands placed by the consumers who use them. The latest dominant technology in this domain is the Next Generation Network (NGN), which is capable of providing converged services with guaranteed QoS whilst offering enormous savings to the network and service providers by reducing their Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) [1]. The majority of the existing Internet traffic is carried over a combination of wireline and wireless communication infrastructure. This infrastructure includes customer premises, telephone exchanges, base stations and core trunk network links.

Corresponding author.

Y. Bi and M.-A. Williams (Eds.): KSEM 2010, LNAI 6291, pp. 518–529, 2010. c Springer-Verlag Berlin Heidelberg 2010

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In essence, the core network of NGN involves a consolidation of several transport networks, each built for a different service, into one core transport network based on IP (Internet Protocol) and MPLS (Multi Protocol Label Switching). The access network is a highly complex mix of wired and wireless technologies, including ADSL (Asymmetric Digital Subscriber Line), FTTP (Fibre-To-The Premises), WLAN (Wireless Local Area Network), WiMAX (Worldwide Interoperability for Microwave Access), etc. The edge network connects the multi-user access network to the high speed core network to complete the NGN architecture. From the architectural, functional and management points of view, NGN has become a highly complex, dynamic and unpredictable system which the traditional Network Management Systems (NMSs) find difficult to control and manage [2]. This situation has led researchers to explore intelligent and autonomic approaches for improving the functionality of NMSs so that they can operate in a more efficient manner to achieve the desired management goals and objectives. Machine learning (ML) is one such approach which has the capability to address the issues of improving the network management functionalities by imparting automated and intelligent data analysis techniques to network management datasets. These techniques have the ability to learn the system behaviour from past data and estimate future behaviour based on the learned system model [3]. In this paper we present the application of a graphical modelling technique, Bayesian Networks (BN), to assist the NMS in solving performance management problems in a network. The original contribution of this paper is the application of BN to model the behaviour of network elements (e.g., the routers) and to provide a mechanism to perform probabilistic inference, decision making and prediction to achieve desired QoS goals through the process of Call Admission Control. The remainder of this paper is structured as follows. In Section 2 we provide the required background and survey some related work in this research domain. Section 3 describes the theory behind BN and the learning algorithms utilised in our solution. In Section 4 we present the conceptual framework of our generic approach using BN-based DSS. We then demonstrate the proof of concept through simulations using OPNET and Hugin tools in Section 5. The results of the simulation, BN model validation and related discussions are presented in Section 6. Section 7 concludes the paper by suggesting possible future work.

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Related Work

The task of a NMS is to monitor and control the functionalities and behaviour of various constituent network elements so as to achieve a desired system state, given a set of pre-defined high-level business goals. The goal could be to maximise resource utilisation (translated as maximising revenue to service providers) or minimise service response times (equivalent to enhanced customer satisfaction). To achieve these objectives, there is a requirement for a standardised and efficient NMS. Several standard NMSs like the SNMP [4], CMIP and FCAPS (Fault, Configuration, Accounting, Performance and Security) [5] are used to manage the telecommunication networks. However, since NGN is a converged

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network providing data and voice services, it would have to be managed by the combination of existing NMSs. One aspect which is common to all NMSs is that they deal with collections of management data typically stored in Management Information Bases (MIBs) at network nodes. To analyse and interpret such massive data sets by a human operator would be almost an impossible task. Hence, we need ML techniques to automate such tasks by utilising rich reasoning mechanisms to find patterns which are useful for network management. Various ML approaches have been proposed to address the issues related to network management functions. Decision trees have been used to achieve proactive network management by mining the data obtained from SNMP MIB objects [6]. Bandwidth broker design can be facilitated by applying Fuzzy Logic [7]. Predictive network fault detection in a telecommunication network has been addressed using Bayesian Belief Networks (BBN) [8]. Dynamic Bayesian Networks (DBN) have also been used for detecting network faults in real-time [9]. Bayesian reasoning based software agents have been used to achieve intelligent network fault management [10]. Reinforcement learning has been used to provide efficient bandwidth allocation in Differentiated Services (Diffserv) networks for per hop behaviour aggregates [11]. Router performance modelling has been achieved using the learning features of Bayesian Belief Networks [12] [13]. An interesting survey on applications of AI techniques to the telecommunications domain that summarises over a decade of research work, is provided in [14]. Based on this survey, it is seen that the majority of research has concentrated on applying ML to the fault management function of the broader network management domain. Also, it is seen that BN are suitable for modelling and studying highly dynamic systems. We now consider the application of BN to implement and enhance the performance management function, which is one of the key functions of management in the FCAPS model of ITU-T [5]. The performance management function which we will be addressing is Call Admission Control and it will be described later in Section 5.

3 3.1

Bayesian Networks Theory Graphical Structure of Bayesian Networks

A BN is a graphical structure that allows us to represent and reason about an uncertain domain. For a set of variables X = {X1 , ..., Xn }, a BN consists of a network structure S that encodes a set of conditional independence assertions about variables in X, and a set P of local probability distributions associated with each variable [15]. An edge from one node to another implies a direct dependency between them, with a child and parent relationship. To quantify the strength of relationships among the random variables, a conditional probability function P is associated with each node, such that P = {p(X1 |Π1 ), ......, p(Xn |Πn )}, where Πi is the parent set of Xi in X. If there is a link from Xi to Xj , then Xi is a parent of Xj and thus it belongs to Πj . For discrete random variables the conditional probability functions are represented as tables, called Conditional Probability Tables (CPTs). For a typical node A, with parents B1 , B2 , ..., Bn ,


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there is associated a CPT given by P (A|B1 , B2 , ..., Bn ). For root nodes, the CPT reduces to prior probabilities. The main principle on which BN work, is Bayes’ rule: P (e|H)P (H) (1) P (H|e) = P (e) where P (H) is the prior belief about a hypothesis, P (e|H) is the likelihood that evidence e results given H, and P (H|e) is the posterior belief in the light of evidence e . This implies that belief concerning a given hypothesis is updated on observing some evidence. 3.2

Features of Bayesian Networks

Structural Learning: The structure of the BN can be constructed manually by the subject expert or through structure learning algorithms - PC (Path Condition) and NPC (Necessary Path Condition) algorithms [16] [17]. The basic idea of these constraint-based algorithms is to derive a set of conditional independence and dependence statements (CIDs) by statistical tests among the nodes of the BN. Parameter Learning: The CPTs (or parameters) can be specified, based on the knowledge of the domain expert, by the process of parameter elicitation. The past data may also be used as the basis for learning the parameters using efficient algorithms. The Expectation Maximization (EM) algorithm is particularly suitable for batch parametric learning [18], while Adaptation algorithms are useful for sequential parameter updates [19]. Inferencing: Evidence on a particular node is used to update the beliefs (posterior probabilities) of other nodes of the BN. The BN framework supports predictive and diagnostic reasoning and uses efficient algorithms for this purpose [20]. Decision-making: To incorporate decision making capabilities, the BN is converted to an influence diagram (ID) by adding decision nodes and utility nodes. The values taken by the decision nodes inform the actions which must be chosen by the decision maker. A utility node quantifies the usefulness of the outcomes resulting from the actions of decision.

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A BN-Based Decision Support System

The domain under consideration for our proposed work is shown in Fig. 1. It consists of two modules, namely, the Network Management System (NMS) and the Decision Support System (DSS). The NMS is assumed to be based on the SNMP protocol, which collects network management data using the SNMP Management Information Base (MIBs) of the network elements. MIB is a virtual database for the managed entities which is defined at various layers of protocol stack and provides management information. In our case, we wish to monitor two data sets, namely MIB data pertaining to the IP layer (Incoming packets,

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Outgoing packets) and SLA (Service Level Agreement) data to check the service level agreements pertaining to the QoS metrics such as Delay, Packet Loss and Jitter. The collected data are then fed from the NMS to the DSS, where the latter builds a model of the network behaviour using the BN framework. The details of the steps involved in the BN model construction are also shown in Fig. 1 through a flowchart. First step is to identify the variables of interest through the process of feature selection. This step eliminates the variables which do not contribute to the model and hence reduces its complexity. Then the continuous variables are discretised into pre-defined number of states. The choice of the number of states is a compromise between modelling accuracy and system complexity. The learning phase involves structural and parameter learning as discussed in Section 3.2. To verify that the model is suitable for estimation, we perform model validation through the process of k-fold cross validation. If the model performs below a pre-defined performance threshold, we increment the size of training data set. Once the desired accuracy is reached, the model is used to make decisions and also to predict future behaviour. These decisions from the BN are then fed back to the NMS to achieve the desired high-level goals as pre-defined through the policy engine. Usually the network manager or the domain expert can make appropriate policies for the required objectives. After the decisions are fed back to the NMS, they are translated into configuration changes which need to be made to the physical network elements.

5 5.1

Case Study and Simulation Setup Motivation

One of the main promises of NGN is the provisioning of guaranteed QoS. It is well known that even extremely well designed networks can suffer from performance


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degradation (reduced QoS) due to the congestion problem. Call Admission Control (CAC) is a preventative approach to deal with congestion, which makes decisions to accept a new call, based on whether this new call can be supported with the desired QoS [21]. A detailed study of the current CAC techniques reveals that there are two major classes of CAC algorithms: Traffic-model-based and Measurement-based [25]. Traffic-model based schemes have the disadvantage that they do not consider the long-range dependence property (i.e., slow decay of the autocorrelation function) which is an important characteristic of the NGN traffic. Measurement-based approaches make admission decisions based on the current network status, which they obtain through periodic measurements of QoS metrics (e.g., end-to-end delay). Hence they can achieve much higher network utilisation and also provide desired QoS. We base our case study on the latter approach and improve it using the BN modelling technique. In our approach, we reduce the overhead of periodic measurements by using the QoS estimates generated from the BN model. 5.2

Simulation Setup

To demonstrate the proof of concept, we used commercial simulation software packages OPNET [22] (for telecommunication network simulation) and Hugin Researcher [23] (for BN modelling). Exchange of simulated data between the software systems was done using a C++ programming framework. The OPNET screenshot of the network topology used for the case study is shown in Fig. 2.

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We consider four clients and four servers connected through switches and routers. The application running on the network is video conferencing under varying TOS (Type Of Service). The router on which we focus is ROUTER A, (which connects to ROUTER B by the bottleneck link) where we capture the MIB data related to network statistics. At this router we implement the BN modelling and then make decisions based on the obtained model. The clients have been modelled with traffic characteristics as detailed in Table 1.

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A. Bashar et al. Table 1. Characteristics of Traffic Sources for Fig. 2 Source Type of Service Priority Client1 Background 32 Client2 Standard 64 Client3 Excellent Effort 96 Client4 Streaming 128

The clients with particular TOS connect to a corresponding server operating under similar TOS. The TOS is determined based on the DSCP (Differentiated Services Code Point) [26] values and it affects the treatment of the packet in the router queues. From Table 1 it can be seen that each TOS has an assigned priority number in the range (0-252) with 0 being the lowest priority (best effort service) and 252 being the highest priority. The higher priority traffic gets through the router first in cases of congestion. The sources generated flows with exponential distributions to simulate traffic as observed in the NGN environment. 5.3

Experimental Details

We ran simulations and collected router MIB statistics at every 60s to obtain historic data. The choice of this sampling rate is inspired by the general practice of network managers who use this practical SNMP polling rate [24]. The details of the statistics collected are given in Table 2, where each statistic corresponds to a node in the BN. Table 2. Network statistics which form the BN nodes (at ROUTER A) BN Node Description Received Traffic received at the router from traffic sources (bytes/sec) Sent Traffic sent from the router to next hop (bytes/sec) Throughput Successful packet transmission rate on the bottleneck link (bits/s) Delay Instantaneous value of packet waiting times in the queue (s) Jitter Packet delay variation in the queues (rate of change of delay)

One of the major operations was to discretise the collected data. The choice of discretisation levels determines the accuracy required for representing the collected data. It is to be understood that, the greater the number of levels, the larger the size of the CPTs, and hence a proportional increase in the model complexity. We chose five discretisation levels with equal bin sizes, and the levels were defined as Very Low (VLO ), Low (LO ), Medium (MED ), High (HI ) and Very High (VHI ). After this process the data were fed into the Hugin Researcher for building the BN models using the algorithms discussed in Section 3.2. Even though in a real network there are many MIB variables (in hundreds) which are monitored and collected, not all of them are relevant when building


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the model. The importance of variables for the model can be determined by the feature selection process which allows us to make a good choice of variables for the prediction task. In our case we began with eight variables and found that only five of them were required for the model (the dropped variables were Bit error rate, Bit errors per packet and Packet loss ratio).

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Simulation Results

We now present the results of the simulation for our case study. The results include the BN models which depict the structure and the parameters, sample admission control decisions and delay predictions made. 6.1

Effect of Number of Training Cases

Our first result shows (Fig. 3) the effect of the number of cases on the construction of the BN model. To check the accuracy of the model, we performed a 10-fold cross validation. We observed the prediction accuracy of all the BN nodes, but present here only the results for the Delay node. This is because we would like to make admission decisions based on the estimates of delay QoS. For this purpose we had to vary the simulation time and capture statistics (number of cases) at a fixed interval of 60s. We started with 100 cases and went up to 3000 cases. The criterion to achieve a suitable model was to get high prediction accuracy with low prediction error deviation. Fig. 3 presents the results of this process and we found that 2000 cases were sufficient to get a stable and accurate model, as increasing cases from 2000 to 2500 improves the prediction accuracy by less than 0.2% at the cost of 500 minutes of simulation time. Hence, we present the BN model for Router A (see Fig. 4), which was obtained from a training data size of 2000 cases.

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Decision Making Using Influence Diagrams

The second result shows how the BN can be used for making decisions under uncertainty. For this purpose, first we convert our BN model (see Fig. 4) into an Influence Diagram. Fig. 5 shows this transformation, where we can observe two extra nodes that are added. The diamond shaped node is called the utility node and the rectangular shaped node is called a decision node (with two actions: Admit or Deny traffic). The utility function defines the measure of goodness of a specific decision and is specified as a table (see Table 3) which quantifies the expert knowledge of making correct decisions. When combined with the observed evidence, this can help us to quantify our decisions in a particular situation. For example, in Fig. 5 the ID makes a decision to Admit a call because the reward of this action (59.4) is greater than the other action (Deny is 40.6) based on observation of a single piece of evidence (Received traffic is in VHI state). This decision can be compared with the decision made in Fig. 6, where we have more information in terms of two pieces of evidence (with additional evidence of Jitter value being in VHI state). In this situation the ID decides to Deny traffic admission, since now the reward for Deny action (80.1) is greater than the Admit action (19.9). This is intuitively a correct decision, because if jitter is high and at the same time there is high incoming traffic, then this traffic needs to be denied so as to respect the QoS constraints. In this way the network manager may take the support of the BN models to cross check the actions which need to be taken in a particular situation. As a matter of fact, we have checked the decisions made by our ID in various scenarios and verified that it does make correct decisions for call admission. However, due to space limitations we are unable to present them here. 5HFHLYHG

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Table 3. Utility Table Decision Admit Deny Delay VLO LO MED HI VHI VLO LO MED HI VHI Utility 100 75 50 25 0 0 25 50 75 100


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Future Predictions of Delay

The final result of our case study is presented in Fig. 7, which shows the prediction capabilities of our BN model with respect to the Delay node variable. We tested four BN models (obtained with training cases of 500, 1000, 1500 and 2000) to predict Delay variable for two different future intervals of 500 cases (from time 2000s to 2500s) and 1000 cases (from time 2000s to 3000s). We assume here that Delay node is unobservable or prohibitively costly to observe. The first observation is that the prediction was better for 500 cases in future as compared to 1000 cases. This is because as more time elapses, the models tend to become obsolete and need to be updated. The second observation is that prediction accuracy improves with the model training size, which is to be expected. The maximum prediction accuracy was 96.85%, which is considered to be significant in terms of saving on the overhead of delay measurements (and instead using estimates from the BN model) for making admission control decisions.

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Fig. 7. Predicting Delay in the future

6.4

Discussion of Results

It has been demonstrated that BN can be used as inference, decision making and prediction technique for network management. We now critically evaluate our approach by considering the following issues. 1. Practicality: The procedure presented is practically realisable, but one issue is the appropriate choice of discretisation of the data for building the BN model. Another issue is the software integration of OPNET and Hugin to test our framework in a real-time scenario. Work is in progress to address these issues. 2. Complexity: The learning algorithms used in our framework are standard and have been optimised for building the BN models, so we can safely conclude that the complexity of our solution is not high.

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3. Speed: This issue needs to be addressed by conducting further experiments on resource utilisation. However, in the cases which we have considered, the time taken for training and decision making have been found to be quite low (a few milliseconds on a PC having Intel P4 CPU with speed of 3.16 GHz). 4. Scalability: To extend this work to large scale networks is an open issue and this will be part of our ongoing work. This discussion leads us to a promising conclusion that the overall benefits which have been achieved using the BN-based DSS framework, outweigh the costs of computational and implementation complexity. This will form the premise for our future work in this domain.

7

Conclusion

This paper has demonstrated that BN are capable of representing dynamic systems, by efficiently modelling unknown and complex relationships between network elements. BN can be used for reasoning, decision making and prediction in the absence of sufficient observable data. We have shown the practicality of BN methodology with the help of a case study related to critical network management function of CAC to guarantee QoS in a heterogeneous IP network. The results demonstrate that BN modelling provides intelligent and automated solutions to improve the functionality of current NMSs. Further work is planned to study the scalability of this methodology to large scale networks. The validation of our simulation-based solution is planned to be performed on a real telecom network. Finally, it would be interesting to compare BN approach to Neural Networks technique in terms of prediction accuracy, model training speed and algorithm complexity.

Acknowledgement The authors would like to acknowledge the support of the University of Ulster and IU-ATC for funding this research work through a Vice Chancellors Research Studentship.

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