Employing Bayesian Belief Networks for Energy Efficient Network Management

Abul Bashar, Mani Subramanian, Santosh Chaudhari, Gerard Parr, Timothy Gonsalves, Sally McClean, Scotney Bryan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

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, the effect on the network performance and the computational complexity of implementation of the solution.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages5
DOIs
Publication statusPublished - 15 Mar 2010
EventNational Conference on Communications - Chennai, India
Duration: 15 Mar 2010 → …

Conference

ConferenceNational Conference on Communications
Period15/03/10 → …

Fingerprint

Network management
Bayesian networks
Quality of service
Computer monitors
Network performance
Computational complexity
Computer systems
Energy utilization

Keywords

  • Bayesian Belief Networks (BBN)
  • Energy-aware
  • Network Management

Cite this

Bashar, A., Subramanian, M., Chaudhari, S., Parr, G., Gonsalves, T., McClean, S., & Bryan, S. (2010). Employing Bayesian Belief Networks for Energy Efficient Network Management. In Unknown Host Publication https://doi.org/10.1109/NCC.2010.5430172
Bashar, Abul ; Subramanian, Mani ; Chaudhari, Santosh ; Parr, Gerard ; Gonsalves, Timothy ; McClean, Sally ; Bryan, Scotney. / Employing Bayesian Belief Networks for Energy Efficient Network Management. Unknown Host Publication. 2010.
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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, the effect on the network performance and the computational complexity of implementation of the solution.",
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note = "Reference text: K. Roth, F. Goldstein, and J. Kleinman, {"}Energy consumption by office and telecommunications equipment in commercial buildings{"}, Vol 1: Energy Consumption Baseline. Arthur D. Little, Reference No. 72895-72900, Jan 2002. {"}Carbon dioxide emissions from the generation of electric power in the United States,{"} US Dept. of Energy and the Environmental Protection Agency, http://www.eia.doe.gov/cneaf/electricity/page/co2-report/co2report.html, July 2000. D. E. Comer, Automated Network Management Systems, 1st Ed., Prentice Hall Co., NJ, USA, 2006. D. Harrington, R. Presuhn, and B. Wijnen, {"}An architecture for describing SNMP management frameworks,{"} RFC 3411, IETF, Dec 2002. M. Gupta and S. Singh, {"}Greening of the Internet,{"} in Proceedings of ACM SIGCOMM, August 2003, pp 19-26. K. Christensen, B. Nordman, and R. Brown {"}Power management in networked devices{"}. IEEE Computer 2004, 37(5):91-93. C. Gunaratne, K. Christensen, and B. Nordman, {"}Managing energy consumption costs in desktop PCs and LAN switches with proxying, split TCP connections, and scaling of link speed{"}, Intl. Journal of Network Management, Vol. 15, No. 5, pp. 297-310, Sept/Oct 2005. M. Gupta and S. Singh, {"}Dynamic ethernet link shutdown for energy conservation on ethernet links,{"} in Proceedings of IEEE ICC, June 2007, pp 6156-6161. L. Chiaraviglio et al., {"}Energy-aware networks: reducing power consumption by switching off network elements{"}, GTTI 2008. Available at: www.gtti.it/GTTI08/papers/chiaraviglio.pdf. C. Gunaratne, K. Christensen, B. Nordman, and S. Suen, {"}Reducing the energy consumption of ethernet with Adaptive Link Rate (ALR),{"} IEEE Transactions on Computers, vol. 57, pp. 448-461, April 2008. P. Mahadevan et al., {"}Energy aware network operations,{"} in Proceedings of IEEE INFOCOM, April 2009, pp 1-6. D. Heckerman, {"}A tutorial on learning with Bayesian networks,{"} Learning in Graphical Models, M. Jordan, ed. MIT Press, Cambridge, MA, 1999. P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, MIT Press, Second edition, Jan 2001. H. Steck, {"}Constrained-based structural learning in Bayesian networks using finite data sets{"}, PhD Thesis, 2001, Institut für der Informatik der Technischen Universität München. F. Jensen, Bayesian Networks and Decision Graphs, 2nd Ed., Springer Co., NY, USA, 2007. K. Murphy, {"}Dynamic Bayesian networks,{"} URL: http://people.cs.ubc.ca/~murphyk/Papers/dbnchapter.pdf, 2002. K. B. Korb and A. E. Nicholson, Bayesian Artificial Intelligence, 1st Ed., CRC Press Co., UK, 2003. A. Bashar, G.P. Parr, S. I. McClean, B.W. Scotney, and D. Nauck, {"}BARD: A novel application of Bayesian reasoning for proactive network management,{"} in Proceedings of the 10th Annual Postgraduate Conference on Telecommunications, Networking and Broadcasting (PGNET 2009), Liverpool, UK, June 2009. S.Y. Wang, C.L. Chou, and C.C. Lin, {"}The design and implementation of the NCTUns network simulation engine{"}, Elsevier Simulation Modelling Practice and Theory, 15 (2007) 57-81. (Pubitemid 46037570) Available at: http://www.hugin.com",
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Bashar, A, Subramanian, M, Chaudhari, S, Parr, G, Gonsalves, T, McClean, S & Bryan, S 2010, Employing Bayesian Belief Networks for Energy Efficient Network Management. in Unknown Host Publication. National Conference on Communications, 15/03/10. https://doi.org/10.1109/NCC.2010.5430172

Employing Bayesian Belief Networks for Energy Efficient Network Management. / Bashar, Abul; Subramanian, Mani; Chaudhari, Santosh; Parr, Gerard; Gonsalves, Timothy; McClean, Sally; Bryan, Scotney.

Unknown Host Publication. 2010.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Employing Bayesian Belief Networks for Energy Efficient Network Management

AU - Bashar, Abul

AU - Subramanian, Mani

AU - Chaudhari, Santosh

AU - Parr, Gerard

AU - Gonsalves, Timothy

AU - McClean, Sally

AU - Bryan, Scotney

N1 - Reference text: K. Roth, F. Goldstein, and J. Kleinman, "Energy consumption by office and telecommunications equipment in commercial buildings", Vol 1: Energy Consumption Baseline. Arthur D. Little, Reference No. 72895-72900, Jan 2002. "Carbon dioxide emissions from the generation of electric power in the United States," US Dept. of Energy and the Environmental Protection Agency, http://www.eia.doe.gov/cneaf/electricity/page/co2-report/co2report.html, July 2000. D. E. Comer, Automated Network Management Systems, 1st Ed., Prentice Hall Co., NJ, USA, 2006. D. Harrington, R. Presuhn, and B. Wijnen, "An architecture for describing SNMP management frameworks," RFC 3411, IETF, Dec 2002. M. Gupta and S. Singh, "Greening of the Internet," in Proceedings of ACM SIGCOMM, August 2003, pp 19-26. K. Christensen, B. Nordman, and R. Brown "Power management in networked devices". IEEE Computer 2004, 37(5):91-93. C. Gunaratne, K. Christensen, and B. Nordman, "Managing energy consumption costs in desktop PCs and LAN switches with proxying, split TCP connections, and scaling of link speed", Intl. Journal of Network Management, Vol. 15, No. 5, pp. 297-310, Sept/Oct 2005. M. Gupta and S. Singh, "Dynamic ethernet link shutdown for energy conservation on ethernet links," in Proceedings of IEEE ICC, June 2007, pp 6156-6161. L. Chiaraviglio et al., "Energy-aware networks: reducing power consumption by switching off network elements", GTTI 2008. Available at: www.gtti.it/GTTI08/papers/chiaraviglio.pdf. C. Gunaratne, K. Christensen, B. Nordman, and S. Suen, "Reducing the energy consumption of ethernet with Adaptive Link Rate (ALR)," IEEE Transactions on Computers, vol. 57, pp. 448-461, April 2008. P. Mahadevan et al., "Energy aware network operations," in Proceedings of IEEE INFOCOM, April 2009, pp 1-6. D. Heckerman, "A tutorial on learning with Bayesian networks," Learning in Graphical Models, M. Jordan, ed. MIT Press, Cambridge, MA, 1999. P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, MIT Press, Second edition, Jan 2001. H. Steck, "Constrained-based structural learning in Bayesian networks using finite data sets", PhD Thesis, 2001, Institut für der Informatik der Technischen Universität München. F. Jensen, Bayesian Networks and Decision Graphs, 2nd Ed., Springer Co., NY, USA, 2007. K. Murphy, "Dynamic Bayesian networks," URL: http://people.cs.ubc.ca/~murphyk/Papers/dbnchapter.pdf, 2002. K. B. Korb and A. E. Nicholson, Bayesian Artificial Intelligence, 1st Ed., CRC Press Co., UK, 2003. A. Bashar, G.P. Parr, S. I. McClean, B.W. Scotney, and D. Nauck, "BARD: A novel application of Bayesian reasoning for proactive network management," in Proceedings of the 10th Annual Postgraduate Conference on Telecommunications, Networking and Broadcasting (PGNET 2009), Liverpool, UK, June 2009. S.Y. Wang, C.L. Chou, and C.C. Lin, "The design and implementation of the NCTUns network simulation engine", Elsevier Simulation Modelling Practice and Theory, 15 (2007) 57-81. (Pubitemid 46037570) Available at: http://www.hugin.com

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N2 - 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, the effect on the network performance and the computational complexity of implementation of the solution.

AB - 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, the effect on the network performance and the computational complexity of implementation of the solution.

KW - Bayesian Belief Networks (BBN)

KW - Energy-aware

KW - Network Management

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DO - 10.1109/NCC.2010.5430172

M3 - Conference contribution

SN - 978-1-4244-6383-1

BT - Unknown Host Publication

ER -

Bashar A, Subramanian M, Chaudhari S, Parr G, Gonsalves T, McClean S et al. Employing Bayesian Belief Networks for Energy Efficient Network Management. In Unknown Host Publication. 2010 https://doi.org/10.1109/NCC.2010.5430172