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Employing Bayesian Belief Networks for Energy Efficient Network Management A. Bashar, G.P. Parr, S.I. McClean and B.W. Scotney

M. Subramanian, S.K. Chaudhari and T.A. Gonsalves

School of Computing and Information Engineering University of Ulster Coleraine, UK BT52 1SA {bashar-a, gp.parr, si.mcclean, bw.scotney}@ulster.ac.uk

Telecommunication and Computer Networking Group Indian Institute of Technology Madras Chennai, India 600036 {manis, skchaudhari, tag}@tenet.res.in

Abstract—Network Management Systems (NMS) are used to monitor the network and along with Operations Support Systems (OSS) maintain the performance with a focus on guaranteeing sustained QoS to the applications and services. One aspect that is given less importance is the energy consumption of the network elements during the off peak periods. This paper looks at a scenario where the NMS plays an important role in making the network energy efficient by intelligently turning the network elements or their selective ports to sleep mode when they are underutilized. To this end, we propose and evaluate a Bayesian Belief Network (BBN) based Decision Management System (DMS), which provides intelligent decisions to the NMS for it to adaptively alter the operational modes of the network elements, without compromising the performance and QoS constraints. Simulated network has been used to provide the proof of concept followed by discussions on the amount of energy saved and its effect on the network performance. Index Terms— Energy-aware, Bayesian Belief Networks (BBN).

Network

Management,

I. INTRODUCTION

T

HE Internet and the telecommunication network infrastructure is growing at a rapid pace and so is its energy consumption. It has been found that the telecommunication and computer network equipment consumes about 12.6 TW-h per year, which is 13% of the total energy used by the Internet and telecoms sector [1]. This consumption translates to an annual cost of about $1 billion in the US alone with proportional CO2 emissions contributing to global warming [2]. As such, researchers from the Internet community have seriously started to work towards reducing energy consumption in network devices. However, with the global deployments of Next Generation Networks (NGN) and the increasing demand from the consumers for multimedia rich applications, the energy expenditure is expected to increase further in the near future. We observe that, the end-to-end Quality of Service (QoS) promised by the NGN is in direct conflict with the objective of saving energy in network equipment, since to provide the required QoS, the network should be up and running most of the time to provide for fault tolerance and resilience. This conflict is explored in this paper,

along with a proposed solution based on an intelligent Network Management System (NMS), which is energy-aware. One of the tasks of NMS [3] is to monitor the performance of network elements under its purview on a regular basis. It is quite possible that based on this observation, the NMS finds a part of the network to be underutilised under certain conditions or times of operation. If this situation is looked at from the energy utilisation point of view, then it can be said the network elements in that part of the network are inefficient, even though they are actively participating in forwarding the traffic which is sent to them. The question which has to be answered is: can the NMS be made intelligent enough to come up with a strategy to proactively put the under utilised network elements (or for that matter, certain ports of the network elements) in sleep mode to conserve energy and at the same time ensure optimised network performance without compromising on QoS guarantees such as maintenance of connected topology and fault-tolerance? The remainder of this paper is structured as follows. In section II we provide the required background and survey some related work in this research domain. In section III we present the details of our proposed framework. Section IV describes the theory behind the machine learning technique called Bayesian Belief Networks (BBN) which is employed in our Decision Management System (DMS). Implementation details and policy level decision-making algorithm are presented in Section V. Simulation methodology and results are presented in section VI. Section VII concludes the paper by suggesting possible future work.

II. BACKGROUND AND RELATED WORK Network Management Systems (NMS) are used to maintain network infrastructure, assure smooth running of services, control the operational costs of the network and provide increased revenues to the service provider. Several standards related to NMS like the SNMP [4], CMIP and FCAPS are used to manage the information and communication technology (ICT) networks. Originally, NMSs were designed to manage networks with the prime focus on fault and

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performance issues. However, with the recent emphasis on making the network more energy efficient, the design of NMS needs to modify its functionalities to bring in the energy factor. One of the earliest works which addresses the need to make the Internet energy-aware is from Gupta et al. [5], where they suggest putting to sleep the underutilised network elements (either the complete router or some of its ports). They state the challenges of sleep mode decision-making and sleep time prediction. Christensen et al. [6] have strongly supported the need for power management in computer networks, which if applied efficiently could be instrumental in saving significant energy. Gunaratne et al. [7] have proposed techniques like proxying, split TCP connections and link speed scaling to conserve energy in desktop PCs and LAN switches. Gupta et al. [8] have implemented the Dynamic Ethernet Link Shutdown (DELS) to show that the percentage of total time that a link can be shutdown can be anywhere from 60% - 80%. Chiaraviglio et al. [9] have proposed some heuristic algorithms to implement power reduction in networks by switching off network elements and links and show that it is possible to save 10% - 25% energy. Gunaratne et al. [10] have shown that an Ethernet link with Adaptive Link Rate (ALR) can operate at a lower data rate for over 80% of the time, yielding significant energy savings with a small increase in packet delay. More recently, Mahadevan et al. [11] have analysed the traffic data from real enterprise and data centre network to simulate and test their energy saving approaches and shown that they could save energy of the order of 16%. Our approach differs from the above related research, by use of machine learning technique to accurately model the network behaviour (utilising the historic traffic patterns) and to adaptively control the operational modes (sleep/active) of the network ports. The motivation for using such an approach was to enable automated power management with real-time decision making capabilities. The next section describes in detail the general framework of our approach.

Fig. 1. The energy-aware network management framework.

sleep and for how long. The policy engine decides the high level policy which needs to be in place for effective decision making. Usually the network manager or the domain expert who has the domain knowledge can make appropriate policies for the required objectives. Once the decisions reach the NMS, it translates them for the CMS, which can actually affect configuration changes to the network elements. The idea of energy-aware network management can be effectively realised if there can be some way of predicting or estimating the future behaviour of the network elements. This prediction will form the basis for proactively putting a particular network element (or its ports) to sleep and rerouting the traffic through other network elements. The prediction task can be performed (by the DMS) based on the observance of past management data, which is readily available to the NMS from the network elements. The huge amounts of network management data which needs to be analysed requires automated systems which can learn efficiently from past data. Hence, we propose to use machine learning technique called Bayesian Belief Networks (BBN), the specific benefits of which are presented in the next section.

III. ENERGY-AWARE MANAGEMENT FRAMEWORK

IV. BAYESIAN BELIEF NETWORKS (BBN)

The proposed energy aware network management framework is shown in Fig. 1. It consists of three modules, namely, the Network Management System (NMS), the Decision Management System (DMS) and the Configuration Management System (CMS). The NMS is the central entity which interacts with the DMS and the CMS. The NMS can be based on the SNMP protocol which collects network management data using the SNMP Management Information Base (MIB) of the network elements. In our case, we concentrate on two main data sets, namely the energy consumption related data (denoted as the Energy Monitor in Fig.1) and the data related to the QoS metrics (denoted as the QoS Monitor in Fig.1). The collected data is then fed from the NMS to DMS, where the latter builds a model of the network behaviour using the BBN framework and provides the decision with regard to which network elements (or their ports) to put to

A. Basics of the BBN theory A BBN is a graphical structure that allows us to represent and reason about an uncertain domain. For a set of variables X = ( X1,...., X n ) , a Bayesian network 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 [12]. An example of a BBN which represents a subset of network behaviour through variables namely, Link Speed, Traffic Rate, Energy Consumed and Buffer Occupancy (as nodes) and three directed edges is shown in Fig. 2. An edge from one node to another implies a direct dependency between them, with a child and parent kind of relationship. To quantify the strength of relationships among the random variables, conditional probability functions are associated with each node, such that

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Fig. 2. An illustrative Bayesian Belief Network.

P = { p( X1 | ∏1),......,p( X n | ∏n )} , where ∏i is the parent set of X i

in X . If there is a link from X i to X j , then X i is a parent of X j and thus it belongs to ∏ j . 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 , there is associated a CPT as given by P( A | B1, B2 ,....Bn ) . The main principle on which BBN work, is Bayes’ rule, P( H | e) =

P(e | H ) P( H ) P(e)

(1)

where P(H ) is the prior belief about a hypothesis H , 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.

B. Learning and Inference features BBNs support three types of learning – structural, parameter and sequential. The structure of the BBN can be constructed manually by the subject matter expert or through structure learning algorithms - PC and NPC algorithms [13] [14]. Parameter learning uses the past data as the basis for learning the parameters through algorithms. One such algorithm known as Expectation Maximization (EM) is particularly useful for parametric learning [15]. In order for the modeled domain to reflect the real domain behavior, the parameters of the model need to be updated based on the observations made. This process is termed as sequential learning [16]. Evidence on a particular node is used to update the beliefs (posterior probabilities) of other nodes of the BBN. The BBN framework supports predictive and diagnostic reasoning and uses efficient algorithms for this purpose [17]. Interested reader can get more details on BBN theory and its application to Call Admission Control in our earlier work [18]. C. Application to our framework A given network is usually highly dynamic in terms of its topology and the supported services running over it. Further, it needs to perform under strict constraints of QoS and policies defined for improved resource utilisation. A large number of performance variables which need to be monitored on a regular basis by the NMS, calls for an intelligent and automated modelling system. In view of these requirements, and the functionalities provided by the BBN, we can see that there is a good match. The conflicting goals of energy savings

Fig. 3. BBN in the context of PBNM.

and QoS provisioning can be very well handled by the BBN owing to the features discussed earlier. As such we illustrate in Fig. 3. , the application of BBN in the context of policy-based network management (PBNM) aimed at reducing energy consumption, while respecting the QoS constraints. V. IMPLEMENTATION ASPECTS

A. Assumptions For the purpose of proof of concept, we start with a basic network topology and light traffic loads as will be detailed later. Although we propose to implement a distributed setup, in the present scope we concentrate on the modelling of a single router. To demonstrate the practicality of our approach, we start with a scenario in which we make single port sleep/wakeup decisions. Also, it is assumed that a dynamic routing protocol (e.g. RIP) computes the updated routes whenever it is decided to change the port status (sleep or awake), without our framework explicitly passing messages to neighbouring routers. Finally, we assume that the framework has topology-awareness and restrictions are in place to make sure that critical links are not put to sleep. B. Policy level algorithm To meet the objective of saving energy by putting the ports of network elements to sleep, we need to design a policy which reroutes the traffic to other ports so as to maintain the required connectivity and QoS. It has been found from experience that, to keep the delay of each link under threshold, the link utilisation should not exceed 70% [11]. Based on this, we come up with the following mathematical model of the policy. Our proposed architecture is distributed in nature. Hence, we concentrate on a single router with n ports, each connected to links having capacity of c i bits/sec, where i = 1,2,....n . The power consumption of a port in active and sleep mode is assumed to be Pactive and Psleep respectively. Let the average incoming and outgoing traffic on each port be in i and out i bits/sec respectively. We monitor and discretize the link utilisations ( U = throughput * 100 % ) as shown in Table I. link capacity

The levels ( U min = 40% and U max = 70%) are selected based on the performance data given in [3]. The following algorithm

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TABLE I Network data which form the BBN nodes Node Name

States

Utilisation_x_y (x = node no. , y = port no.)

{low, med, high}

Details low (0 - 40%) med (41 - 69%) high (70 - 100%) Port 1

implements the policy which makes the sleep and wakeup decisions for the ports of the router.

Port 4

Port 2

Ui =

totali * 100 % (where, total i = in i + out i ) ci

Step 2: Build the BBN model (both the structure and CPTs). Step 3: Find the port with the least utilisation i.e. the node in the BBN which has maximum value for the marginal probability P(low). Put the corresponding port to sleep (assuming RIP updates the routes). Step 4: Update the BBN model at regular intervals and keep monitoring probability P(high) of other BBN nodes. Step 5: If any other node’s P(high) exceeds a predefined threshold, wake up the sleeping port, otherwise leave it sleeping. Go to Step 4.

VI. SIMULATION RESULTS The domain under consideration for our proposed work is shown in Fig. 4. The network comprises of Customer Premises Equipment (CPE) (hosts which generate or receive traffic, e.g. 12, 14, 18), access network (switches, e.g. 6, 8, 9) and core network (routers, e.g. 1, 2, 3). We assume that all the network elements are SNMP-enabled and they have SNMP clients installed in them for sending MIB data to the NMS. A. Simulation Setup NCTUns (National Chiao Tung University network simulator) [19] was used to simulate the network topology as shown in Fig. 4. The choice of the topology was based on the criteria of redundant links across the hosts which guarantee connectivity even when some ports are put to sleep mode. The queue length for all the routers was set to 50 packets and the ports were connected with bidirectional link with a capacity of 10 Mbps. Hugin Researcher [20] was used to generate the BBN model from the network data. The learning and inference algorithms mentioned in section IV-B, which form the Hugin Decision Engine, were used in our framework through C++ APIs. B. Traffic Characteristics To simulate a hybrid traffic in NCTUns with maximum randomness, the source hosts were modeled as a Poisson process as shown in Table II. Sources with node IDs 10, 11 and 12 were generating (unidirectional) traffic and sending to destination hosts having IDs 20, 14, and 18 respectively. This setup was chosen so as to make sure that all links connected to Router 2 (which is central to the core network with marked port numbers), were utilized for sending traffic.

Port 3

Step 1: For each port i = 1,2,...n , monitor the link utilisation

Fig. 4. Network topology simulated in NCTUns.

C. Experimental Details We chose simulated time of our experiment to be 10000s, and collected statistics to train the BBN in the DMS. In our simulator, we collected the data as shown in Table I, at regular TABLE II Characteristics of traffic sources at each host Parameter Packet Interarrival Time (s) Packet Size (bytes) Traffic Start Time (s) Traffic ON state time (s) Traffic OFF state time (s)

Value Exponential (0.001) Exponential (1024) Constant (1.0) Constant (60.0) Constant (30.0)

intervals of 10s. The DMS module then learned the BBN model (both structure and parameter) through the observed data. To capture the real-time behavior, re-training of the model was performed at an interval of 1000s. Even though in a real network there are many MIB variables (literally in hundreds for each element) which are monitored and collected, this experimental setup presents the general approach which can easily be extended to more variables. Based on the collected data we got the BBN structure (using the PC algorithm) for Router 2 as shown in Fig. 5. The marginal probabilities are shown in the boxes besides the nodes (obtained from EM algorithm). Taking into account the space limitations and clarity of presentation, we prefer to display the marginal probabilities as opposed to the CPTs. From the figure it is seen that port 3 has the lowest utilisation, P(low) = 0.933 (the highest probability of being in low state). So we made a decision to put port 3 to sleep mode at 2000s. Fig. 6 shows a sample average throughput plot for all the ports of Router 2. Throughput of port 3 goes down and stays down, as the marginal probability P (high) of other ports are below the predefined threshold (0.01) in this sample run. Updated routes cause a decrease in traffic at ports 2 and 4. Fig. 7 shows the power consumption of Router 2 in two scenarios (DMS is OFF and DMS is ON). We consider power ratings of a switch [11]

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delay. This base work encourages us to extend the framework to model multiple routers in a distributed BBN setup which is topology-aware, with provision for multiple sleeping ports to maximize energy saving. There is also a need to study the effect of offered network load on the amount of energy saving. ACKNOWLEDGMENT The authors would like to acknowledge the support of the IU-ATC (http://www.iu-atc.com). Specific acknowledgment is given to our British Council-UKIERI-DST Research Award (SA-07-0083) that funded the internship for this joint research.

Fig. 5. BBN for Router 2. 900 Port1

Port2

Port3

REFERENCES

Port4

A v e r a g e T h r o u g h p u t (K B /s e c )

800

[1]

700 600

[2]

500 400 300 200

[3]

100

[4]

0 0

2000

4000

6000

8000

10000

Time (sec)

Fig. 6. Average throughput at the ports of Router 2.

[5] [6]

3 Power consumed: DMS is ON

[7]

Power consumed: DMS is OFF

P o w e r (W a tts )

2.5 2

[8] 1.5

[9]

1 0.5

[10]

0 0

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4000

6000

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Time (sec)

Fig. 7. Power consumed by the ports of Router 2.

and use Pactive = 0.42 watt and Psleep = 0 watt for a 10Mbps link. We measured the average round-trip time (for active and sleep modes), RTTUP =126.567 ms and RTTDOWN = 130.248 ms for the link connected to port 3, which meant an incremental delay of 2.9%, in comparison to a power saving of 25% per sleeping port for Router 2. This energy saving could be small relative to the total power consumed by the router and needs further investigation. However, the saving is significant when compared to a small reduction in the QoS constraint of delay. VII. CONCLUSION The simulation results of this paper obtained from a proof of concept study show that BBN can easily model the real-time network behavior and help us in deciding which ports to put to sleep. It has been shown that a power saving of about 25% per port is achieved at a cost of about 3% increase in average

[11] [12]

[13] [14]

[15] [16] [17] [18]

[19]

[20]

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