Abstract
Network Management Systems (NMS) are used to monitor the network and maintain its performance with a prime focus on guaranteeing sustained QoS to the services. However, another aspect that must be given due importance is the energy consumption of the network elements, specially during the off-peak periods. This paper proposes and implements a novel idea of energy-aware network management that looks at a scenario where the NMS plays an important role in making the network energy efficient by predictively turning the network elements to sleep mode when they are underutilized. To this end, it designs and evaluates a Bayesian Networks (BN) based Intelligent Traffic Engineering (BNITE) solution, which provides intelligent decisions to the NMS for it to adaptively alter the operational modes of the network elements, with minimum compromise in the network performance and QoS guarantees. Energy-aware Traffic Engineering algorithms are developed for both stand-alone (single router) and centralised (multiple routers) scenarios to prove the concept. Simulated network experiments using NCTUns and Hugin Researcher have been used to demonstrate the feasibility and practicality of the proposed solution. Significant energy savings with minimal degradation in QoS metrics demonstrate the benefits of BNITE solution for real-world networks such as the NGN.
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References
Roth, K., Goldstein, F., Kleinman, J.: Energy consumption by office and telecommunications equipment in commercial buildings. In: Energy Consumption Baseline, Arthur D. Little, Reference No. 72895-00 (2002)
US Dept. of Energy and the Environmental Protection Agency: Carbon dioxide emissions from the generation of electric power in the United States (2000), http://www.eia.doe.gov/cneaf/electricity/page/co2_report/co2report.html
Comer, D.E.: Automated Network Management Systems. Prentice Hall Co., NJ (2006)
Harrington, D., Presuhn, R., Wijnen, B.: An architecture for describing SNMP management frameworks, RFC 3411, IETF (2002)
Gupta, M., Singh, S.: Greening of the Internet. In: ACM SIGCOMM 2003, pp. 19–26 (2003)
Christensen, K., Nordman, B., Brown, R.: Power management in networked devices. IEEE Computer 37(5), 91–93 (2004)
Gunaratne, C., Christensen, K., Nordman, B.: Managing energy consumption costs in desktop PCs and LAN switches with proxying, split TCP connections, and scaling of link speed. International Journal of Network Management 15(5), 297–310 (2005)
Gupta, M., Singh, S.: Dynamic ethernet link shutdown for energy conservation on ethernet links. In: IEEE ICC 2007, pp. 6156–6161 (2007)
Chiaraviglio, L., et al.: Energy-aware networks: Reducing power consumption by switching off network elements. In: GTTI 2008 (2008), http://www.gtti.it/GTTI08/papers/chiaraviglio.pdf
Gunaratne, C., Christensen, K., Nordman, B., Suen, S.: Reducing the energy consumption of ethernet with Adaptive Link Rate (ALR). IEEE Transactions on Computers 57, 448–461 (2008)
Mahadevan, P., et al.: Energy aware network operations. In: IEEE INFOCOM 2009, pp. 1–6 (2009)
Bashar, A., et al.: Employing Bayesian belief networks for energy efficient network management. In: IEEE National Conference on Communications (NCC 2010), pp. 1–5 (2010)
Wang, S.Y., Chou, C.L., Lin, C.C.: The design and implementation of the NCTUns network simulation engine. Elsevier Simulation Modelling Practice and Theory 15(1), 57–81 (2007)
Hugin Expert A/S: Hugin Researcher 7.3. (2011), http://www.hugin.com
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Bashar, A. (2012). BNITE: Bayesian Networks-Based Intelligent Traffic Engineering for Energy-Aware NGN. In: Benlamri, R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30507-8_14
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DOI: https://doi.org/10.1007/978-3-642-30507-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30506-1
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