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Energy Aware Network: Bayesian Belief Networks Based Decision Management System


Affiliations
1 Department of Computer Science and Engineering, Indian Institute of Technology Madras, India
     

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A Network Management System (NMS) plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors&maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring&maintaining networks like improving QoS&saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware.

We propose a Decision Management System (DMS) framework which uses a machine learning technique called Bayesian Belief Networks (BBN), to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.


Keywords

Energy Aware Network Management System (EA-NMS), Next Generation Networks (NGN), Bayesian Belief Networks (BBN), Decision Management System (DMS).
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  • Energy Aware Network: Bayesian Belief Networks Based Decision Management System

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Authors

Santosh Kumar Chaudhari
Department of Computer Science and Engineering, Indian Institute of Technology Madras, India
Hema A. Murthy
Department of Computer Science and Engineering, Indian Institute of Technology Madras, India

Abstract


A Network Management System (NMS) plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors&maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring&maintaining networks like improving QoS&saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware.

We propose a Decision Management System (DMS) framework which uses a machine learning technique called Bayesian Belief Networks (BBN), to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.


Keywords


Energy Aware Network Management System (EA-NMS), Next Generation Networks (NGN), Bayesian Belief Networks (BBN), Decision Management System (DMS).