Performance Evaluation of Green Data Centre Management Supporting Sustainable Growth of the Internet of Things

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23 Citations (Scopus)

Abstract

Network management is increasingly being customised for green objectives due to roll out of mission-critical applications across the internet of things and execution, in a number of cases, on battery-constrained devices. In addition, the volume of operations across the internet of things is attracting climate change concerns. While operational efficiency of wireless devices and in data centres (which support operation of the internet of things) should not be achieved at the expense of Quality of Service, optimisation opportunities should be exploited and inefficient resource use minimised. Green networking approaches however, are not yet standardised, and there is scope for novel middleware architectures. In this paper, we explore operational efficiency from the perspective of activities in data centres which support the internet of things. This includes evaluation of the effectiveness of mechanisms integrated into the e-CAB framework, an algorithm proposed by the authors to manage next generation data centres with green objectives. A selection of its policy mechanisms have been implemented in the NS-2 Network Simulator to evaluateperformance; configuration decisions are described in this paper and presented alongsideexperimental results which demonstrate optimisations achieved. Focus lies, in particular,on rate adaptation of its context discovery protocol which is responsible for capturingreal-time network state. Performance results reveal a small overhead when applying networkmanagement and validate improved efficiency through adaption in response to environmentdynamics.
LanguageEnglish
Pages221-242
JournalSimulation Modelling Practice and Theory
Volume34
DOIs
Publication statusPublished - 31 Jan 2013

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Network management
Middleware
Climate change
Quality of service
Simulators
Network protocols
Internet of things

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@article{b81379d1fb35423fb9c1d6019dec600c,
title = "Performance Evaluation of Green Data Centre Management Supporting Sustainable Growth of the Internet of Things",
abstract = "Network management is increasingly being customised for green objectives due to roll out of mission-critical applications across the internet of things and execution, in a number of cases, on battery-constrained devices. In addition, the volume of operations across the internet of things is attracting climate change concerns. While operational efficiency of wireless devices and in data centres (which support operation of the internet of things) should not be achieved at the expense of Quality of Service, optimisation opportunities should be exploited and inefficient resource use minimised. Green networking approaches however, are not yet standardised, and there is scope for novel middleware architectures. In this paper, we explore operational efficiency from the perspective of activities in data centres which support the internet of things. This includes evaluation of the effectiveness of mechanisms integrated into the e-CAB framework, an algorithm proposed by the authors to manage next generation data centres with green objectives. A selection of its policy mechanisms have been implemented in the NS-2 Network Simulator to evaluateperformance; configuration decisions are described in this paper and presented alongsideexperimental results which demonstrate optimisations achieved. Focus lies, in particular,on rate adaptation of its context discovery protocol which is responsible for capturingreal-time network state. Performance results reveal a small overhead when applying networkmanagement and validate improved efficiency through adaption in response to environmentdynamics.",
author = "Cathryn Peoples and Gerard Parr and Sally McClean and BW Scotney and PJ Morrow",
note = "Reference text: [1] A. Pantelopoulos, N.G. Bourbakis, Prognosis – a wearable health-monitoring system for people at risk: methodology and modeling, IEEE Transactions on Information Technology in Biomedicine 14 (3) (2010) 613–621. doi http://dx.doi.org/10.1109/TiTB.2010.2040085. [2] S. Ricciardi, D. Careglio, G. Santos-Boada, J. Sole-Pareta, U. Fiore, F. Palmieri, Saving energy in data center infrastructures, in: Proc. of 1st Int. Conf. on Data Compression, Communications and Processing, June 2011, pp. 265–270. doi: http://dx.doi.org/10.1109/CCP.2011.9. [3] United Nations, Working Together Saving Tomorrow Today, December 2011. <www.cop17-cmp7durban.com> (accessed 18.12.12). [4] M. Karir, I. Foo, Data Center Reference Architectures, Work in Progress as an IETF Internet Draft, October 2011. [5] H.F. Hamann, V. Lo9 pez, A. Stepanchuk, Thermal zones for more efficient data center energy management, in: Proc. of 12th IEEE Intersociety Conf. on Thermal and Thermomechanical Phenomena in Electronic Systems, 2010, pp. 1–6. doi: http://dx.doi.org/10.1109/ITHERM.2010.550133 2. [6] S. Ricciardi, D. Careglio, G. Santos-Boada, J. Sol{\'e}-Pareta, U. Fiore, F. Palmieri, Saving energy in data center infrastructures, in: Proc. of 1st Int. Conf. on Data Compression, Communications and Processing, 2011, pp. 265–270. doi: http://dx.doi.org/10.1109/CCP.2011.9. [7] C. Peoples, G. Parr, S. McClean, Context-aware characterisation of energy consumption in data centres, in: Proc. of 3rd IFIP/IEEE Int. Workshop on Management of the Future Internet (ManFI), Dublin, May 2011, pp. 1250–1257. doi: http://dx.doi.org/10.1109/INM.2011.5990573. [8] C. Peoples, G. Parr, S. McClean, Energy-aware data centre management, in: Proc. of National Conference on Communications, January 2011, pp. 1–5. doi: http://dx.doi.org/10.1109/NCC.2011.5734700. [9] NS-2, The Network Simulator. <http://www.isi.edu/nsnam/ns/> (accessed 18.12.12). [10] C. Peoples, G. Parr, A. Schaeffer-Filho, A. Mauthe, Towards the simulation of energy-efficient resilience management, in: Proc. of 4th Int. ICST Conf. on Simulation Tools and Techniques (SimuTools), Barcelona, March 2011, pp. 1–6. ISBN: 978-1-936968-00-8. [11] V. Manral, Benchmarking Power Usage of Networking Devices, Work in Progress as an IETF Internet Draft, January 2011. [12] J. Moses, R. Iyer, R. Illikkal, S. Srinivasan, K. Aisopos, Shared resource monitoring and throughput optimisation in cloud-computing data centers, in: Proc. of Int. Parallel & Distributed Processing, Symposium, May 2011, pp. 1024–1033. doi: http://dx.doi.org/10.1109/IPDPS.2011.98. [13] J. Li, K. Shuang, S. Su, Q. Huang, P. Xu, X. Cheng, J. Wang, Reducing operational costs through consolidation with resource prediction in the cloud, in: Proc. of 12th IEEE/ACM Int. Symp. on Cluster, Cloud and Grid, Computing, May 2012, pp. 793–798. doi: http://dx.doi.org/10.1109/CCGrid.2012.50. [14] N. Ahuja, C. Rego, S. Ahuja, M. Warner, A. Docca, Data center efficiency with higher ambient temperatures and optimised cooling control, in: Proc. of 27th IEEE Semiconductor Thermal Measurement and Management, Symposium, Mar. 2011, pp. 105–109. doi: http://dx.doi.org/10.1109/STHERM.2011.5767186. [15] K. Muroya, T. Kinoshita, H. Tanaka, M. Youro, Power reduction effect of higher room temperature operation in data centres, in: Proc. of IEEE Network Operations and Management, Symposium, April 2010, pp. 661–673. doi: http://dx.doi.org/10.1109/NOMS.2010.5488420. [16] I. Dumitru, I. Fagarasan, S. Iliescu, Y. Hadj Said, S. Ploix, Increasing energy efficiency in data centers using energy management, in: Proc. of IEEE/ACM Int. Conf. on Green Computing and, Communications, 2011, pp. 159–165. doi: http://dx.doi.org/10.1109/GreenCom.011.53. [17] D. Brinza, A. Zelikovsky, DEEPS: deterministic energy-efficient protocol for sensor networks, in: Proc. of 7th ACIS Int. Conf. on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed, Computing, June 2006, pp. 261–266. doi: http://dx.doi.org/10.1109/SNPD_SAWN.2006.31. [18] N. Lin, X-M. Guan, L-X. Song, Research and simulation of dynamic resource management algorithm, in: Proc. of 9th Int. Conf. on Hybrid Intelligent Systems, August 2009, pp. 471–474. doi: http://dx.doi.org/10.1109/HIS.2009.97. [19] A. Jabas, R.M. Garimella, S. Ramachandram, Proposing an enhanced mobile ad hoc network framework to the open source simulator NS2, in: Proc. of Mosharaka Int. Conf. on Communications, Computers and Applications, August 2008, pp. 14–19. doi: http://dx.doi.org/10.1109/MICCCA.2008.4669843. [20] W. Bo, H. Chuanhe, Y. Wenzhong, A novel routing protocol based on an energy model in ad hoc networks, in: Proc. of 5th Int. Conf. on Wireless Communications, Networking and Mobile, Computing, September 2009, pp. 4–4. doi: http://dx.doi.org/10.1109/WICOM.2009.5303268. [21] M.E.J. Newman, Power Laws, Pareto Distributions and Zipf’s Law, Contemporary Physics, vol. 46, no. 5, September–October 2005, pp. 323–351. doi: http://dx.doi.org/10.1080/0010751050002444.",
year = "2013",
month = "1",
day = "31",
doi = "10.1016/j.simpat.2012.12.008",
language = "English",
volume = "34",
pages = "221--242",

}

TY - JOUR

T1 - Performance Evaluation of Green Data Centre Management Supporting Sustainable Growth of the Internet of Things

AU - Peoples, Cathryn

AU - Parr, Gerard

AU - McClean, Sally

AU - Scotney, BW

AU - Morrow, PJ

N1 - Reference text: [1] A. Pantelopoulos, N.G. Bourbakis, Prognosis – a wearable health-monitoring system for people at risk: methodology and modeling, IEEE Transactions on Information Technology in Biomedicine 14 (3) (2010) 613–621. doi http://dx.doi.org/10.1109/TiTB.2010.2040085. [2] S. Ricciardi, D. Careglio, G. Santos-Boada, J. Sole-Pareta, U. Fiore, F. Palmieri, Saving energy in data center infrastructures, in: Proc. of 1st Int. Conf. on Data Compression, Communications and Processing, June 2011, pp. 265–270. doi: http://dx.doi.org/10.1109/CCP.2011.9. [3] United Nations, Working Together Saving Tomorrow Today, December 2011. <www.cop17-cmp7durban.com> (accessed 18.12.12). [4] M. Karir, I. Foo, Data Center Reference Architectures, Work in Progress as an IETF Internet Draft, October 2011. [5] H.F. Hamann, V. Lo9 pez, A. Stepanchuk, Thermal zones for more efficient data center energy management, in: Proc. of 12th IEEE Intersociety Conf. on Thermal and Thermomechanical Phenomena in Electronic Systems, 2010, pp. 1–6. doi: http://dx.doi.org/10.1109/ITHERM.2010.550133 2. [6] S. Ricciardi, D. Careglio, G. Santos-Boada, J. Solé-Pareta, U. Fiore, F. Palmieri, Saving energy in data center infrastructures, in: Proc. of 1st Int. Conf. on Data Compression, Communications and Processing, 2011, pp. 265–270. doi: http://dx.doi.org/10.1109/CCP.2011.9. [7] C. Peoples, G. Parr, S. McClean, Context-aware characterisation of energy consumption in data centres, in: Proc. of 3rd IFIP/IEEE Int. Workshop on Management of the Future Internet (ManFI), Dublin, May 2011, pp. 1250–1257. doi: http://dx.doi.org/10.1109/INM.2011.5990573. [8] C. Peoples, G. Parr, S. McClean, Energy-aware data centre management, in: Proc. of National Conference on Communications, January 2011, pp. 1–5. doi: http://dx.doi.org/10.1109/NCC.2011.5734700. [9] NS-2, The Network Simulator. <http://www.isi.edu/nsnam/ns/> (accessed 18.12.12). [10] C. Peoples, G. Parr, A. Schaeffer-Filho, A. Mauthe, Towards the simulation of energy-efficient resilience management, in: Proc. of 4th Int. ICST Conf. on Simulation Tools and Techniques (SimuTools), Barcelona, March 2011, pp. 1–6. ISBN: 978-1-936968-00-8. [11] V. Manral, Benchmarking Power Usage of Networking Devices, Work in Progress as an IETF Internet Draft, January 2011. [12] J. Moses, R. Iyer, R. Illikkal, S. Srinivasan, K. Aisopos, Shared resource monitoring and throughput optimisation in cloud-computing data centers, in: Proc. of Int. Parallel & Distributed Processing, Symposium, May 2011, pp. 1024–1033. doi: http://dx.doi.org/10.1109/IPDPS.2011.98. [13] J. Li, K. Shuang, S. Su, Q. Huang, P. Xu, X. Cheng, J. Wang, Reducing operational costs through consolidation with resource prediction in the cloud, in: Proc. of 12th IEEE/ACM Int. Symp. on Cluster, Cloud and Grid, Computing, May 2012, pp. 793–798. doi: http://dx.doi.org/10.1109/CCGrid.2012.50. [14] N. Ahuja, C. Rego, S. Ahuja, M. Warner, A. Docca, Data center efficiency with higher ambient temperatures and optimised cooling control, in: Proc. of 27th IEEE Semiconductor Thermal Measurement and Management, Symposium, Mar. 2011, pp. 105–109. doi: http://dx.doi.org/10.1109/STHERM.2011.5767186. [15] K. Muroya, T. Kinoshita, H. Tanaka, M. Youro, Power reduction effect of higher room temperature operation in data centres, in: Proc. of IEEE Network Operations and Management, Symposium, April 2010, pp. 661–673. doi: http://dx.doi.org/10.1109/NOMS.2010.5488420. [16] I. Dumitru, I. Fagarasan, S. Iliescu, Y. Hadj Said, S. Ploix, Increasing energy efficiency in data centers using energy management, in: Proc. of IEEE/ACM Int. Conf. on Green Computing and, Communications, 2011, pp. 159–165. doi: http://dx.doi.org/10.1109/GreenCom.011.53. [17] D. Brinza, A. Zelikovsky, DEEPS: deterministic energy-efficient protocol for sensor networks, in: Proc. of 7th ACIS Int. Conf. on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed, Computing, June 2006, pp. 261–266. doi: http://dx.doi.org/10.1109/SNPD_SAWN.2006.31. [18] N. Lin, X-M. Guan, L-X. Song, Research and simulation of dynamic resource management algorithm, in: Proc. of 9th Int. Conf. on Hybrid Intelligent Systems, August 2009, pp. 471–474. doi: http://dx.doi.org/10.1109/HIS.2009.97. [19] A. Jabas, R.M. Garimella, S. Ramachandram, Proposing an enhanced mobile ad hoc network framework to the open source simulator NS2, in: Proc. of Mosharaka Int. Conf. on Communications, Computers and Applications, August 2008, pp. 14–19. doi: http://dx.doi.org/10.1109/MICCCA.2008.4669843. [20] W. Bo, H. Chuanhe, Y. Wenzhong, A novel routing protocol based on an energy model in ad hoc networks, in: Proc. of 5th Int. Conf. on Wireless Communications, Networking and Mobile, Computing, September 2009, pp. 4–4. doi: http://dx.doi.org/10.1109/WICOM.2009.5303268. [21] M.E.J. Newman, Power Laws, Pareto Distributions and Zipf’s Law, Contemporary Physics, vol. 46, no. 5, September–October 2005, pp. 323–351. doi: http://dx.doi.org/10.1080/0010751050002444.

PY - 2013/1/31

Y1 - 2013/1/31

N2 - Network management is increasingly being customised for green objectives due to roll out of mission-critical applications across the internet of things and execution, in a number of cases, on battery-constrained devices. In addition, the volume of operations across the internet of things is attracting climate change concerns. While operational efficiency of wireless devices and in data centres (which support operation of the internet of things) should not be achieved at the expense of Quality of Service, optimisation opportunities should be exploited and inefficient resource use minimised. Green networking approaches however, are not yet standardised, and there is scope for novel middleware architectures. In this paper, we explore operational efficiency from the perspective of activities in data centres which support the internet of things. This includes evaluation of the effectiveness of mechanisms integrated into the e-CAB framework, an algorithm proposed by the authors to manage next generation data centres with green objectives. A selection of its policy mechanisms have been implemented in the NS-2 Network Simulator to evaluateperformance; configuration decisions are described in this paper and presented alongsideexperimental results which demonstrate optimisations achieved. Focus lies, in particular,on rate adaptation of its context discovery protocol which is responsible for capturingreal-time network state. Performance results reveal a small overhead when applying networkmanagement and validate improved efficiency through adaption in response to environmentdynamics.

AB - Network management is increasingly being customised for green objectives due to roll out of mission-critical applications across the internet of things and execution, in a number of cases, on battery-constrained devices. In addition, the volume of operations across the internet of things is attracting climate change concerns. While operational efficiency of wireless devices and in data centres (which support operation of the internet of things) should not be achieved at the expense of Quality of Service, optimisation opportunities should be exploited and inefficient resource use minimised. Green networking approaches however, are not yet standardised, and there is scope for novel middleware architectures. In this paper, we explore operational efficiency from the perspective of activities in data centres which support the internet of things. This includes evaluation of the effectiveness of mechanisms integrated into the e-CAB framework, an algorithm proposed by the authors to manage next generation data centres with green objectives. A selection of its policy mechanisms have been implemented in the NS-2 Network Simulator to evaluateperformance; configuration decisions are described in this paper and presented alongsideexperimental results which demonstrate optimisations achieved. Focus lies, in particular,on rate adaptation of its context discovery protocol which is responsible for capturingreal-time network state. Performance results reveal a small overhead when applying networkmanagement and validate improved efficiency through adaption in response to environmentdynamics.

U2 - 10.1016/j.simpat.2012.12.008

DO - 10.1016/j.simpat.2012.12.008

M3 - Article

VL - 34

SP - 221

EP - 242

ER -