An Energy Aware Network Management Approach using Server Profiling in 'Green' Clouds

Cathryn Peoples, Gerard Parr, Sally McClean, BW Scotney, PJ Morrow, Santosh Chaudhari, Ravi Theja

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

6 Citations (Scopus)

Abstract

Clouds and data centres are significant consumers of power. There are however, opportunities for optimising carbon cost here as resource redundancy is provisioned extensively. Data centre resources, and subsequently clouds which support them, are traditionally organised into tiers; switch-off activity when managing redundant resources therefore occurs in an approach which exploits cost advantages associated with closing down entire network portions. We suggest however, an alternative approach to optimise cloud operation while maintaining application QoS: Simulation experiments identify that network operation can be optimised by selecting servers which process traffic at a rate that more closely matches the packet arrival rate, and resources which provision excessive capacity additional to that required may be powered off for improved efficiency. This recognises that there is a point in server speed at which performance is optimised, and operation which is greater than or less than this rate will not achieve optimisation. A series of policies have been defined in this work for integration into cloud management procedures; performance results from their implementation and evaluation in simulation show improved efficiency by selecting servers based on these relationships.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages8
Publication statusPublished - 31 Dec 2012
EventIEEE Second Symposium on Network Cloud Computing and Applications - London, UK
Duration: 31 Dec 2012 → …

Conference

ConferenceIEEE Second Symposium on Network Cloud Computing and Applications
Period31/12/12 → …

Fingerprint

Network management
Servers
Redundancy
Costs
Quality of service
Switches
Carbon
Experiments

Keywords

  • autonomic content distribution
  • cloud data centre
  • context awareness
  • dynamic configuration
  • energy tolerance
  • policy-based management
  • NS-2
  • Opnet
  • self-managing platform.

Cite this

@inproceedings{80cecf7742114a65af071c4122527de8,
title = "An Energy Aware Network Management Approach using Server Profiling in 'Green' Clouds",
abstract = "Clouds and data centres are significant consumers of power. There are however, opportunities for optimising carbon cost here as resource redundancy is provisioned extensively. Data centre resources, and subsequently clouds which support them, are traditionally organised into tiers; switch-off activity when managing redundant resources therefore occurs in an approach which exploits cost advantages associated with closing down entire network portions. We suggest however, an alternative approach to optimise cloud operation while maintaining application QoS: Simulation experiments identify that network operation can be optimised by selecting servers which process traffic at a rate that more closely matches the packet arrival rate, and resources which provision excessive capacity additional to that required may be powered off for improved efficiency. This recognises that there is a point in server speed at which performance is optimised, and operation which is greater than or less than this rate will not achieve optimisation. A series of policies have been defined in this work for integration into cloud management procedures; performance results from their implementation and evaluation in simulation show improved efficiency by selecting servers based on these relationships.",
keywords = "autonomic content distribution, cloud data centre, context awareness, dynamic configuration, energy tolerance, policy-based management, NS-2, Opnet, self-managing platform.",
author = "Cathryn Peoples and Gerard Parr and Sally McClean and BW Scotney and PJ Morrow and Santosh Chaudhari and Ravi Theja",
note = "Reference text: [1] A. Mehta, M. Menaria, S. Dangi and S. Rao, “Energy Conservation in Cloud Infrastructures,” in Proc. of IEEE Int. Systems Conf., Apr. 2011, pp. 456-460. [2] U. Lee, I. Rimac, D. Kilper and V. Hilt, “Toward Energy-Efficient Content Dissemination,” in IEEE Network, Mar/Apr. 2011, Vol. 25, Iss. 2, pp. 14-19. [3] HM Treasury, “Autumn Statement 2011,” Nov. 2011; Available: www.hm-treasury.gov.uk. [4] Government of India, “Key Features of Budget 2012-2013,” Mar. 2012; Available: indiabudget.nic.in. [5] M. Adachi, T. Hiraoka and N. Komatsu, “A Study on a Resource Allocation Algorithm for On-demand Data Center Services,” in Proc. of Int. Conf. on Advanced Comm. Technology, Feb. 2008, pp. 295-300. [6] Opnet, “Opnet Modeler Accelerating Network R&D (Network Simulation)”; Available: http://www.opnet.com/. [7] C. Peoples, G. Parr and S. McClean, “Context-aware Characterisation of Energy Consumption in Data Centres,” in Proc. of IEEE Int. Workshop on Management of the Future Internet, May 2011, pp. 1250-1257. [8] C. Peoples, G. Parr and S. McClean, “Energy-aware Data Centre Management,” in Proc. of National Conference on Communications, Jan. 2011, pp. 1-5. [9] Hewlett-Packard Corporation, Intel Corporation, Microsoft Corporation, Phoenix Technologies Limited, and Toshiba Corporation, “Advanced Configuration and Power Interface Specification,” Revision 4.0a,. 2010. [10] Distributed Management Task Force; Available: www.dmtf.org. [11] Distributed Management Task Force, “CIM Schema,” Feb. 2011. [12] Intel, “Intel Intelligent Power Node Manager”; Available: www.intel.com/technology/nodemanager/. [13] Intel, “Intel Data Center Manager”; Available: software.intel.com/. [14] Intel, “Data Center Energy Efficiency with Intel Power Management Technologies”; Available: http://software.intel.com/. [15] IBM, “IBM PowerExecutive”; Available: www-03.ibm.com. [16] J. Parello and B. Claise, “Energy-aware Networks and Devices MIB,” ‘work in progress’ as IETF Internet Draft, Oct. 2011. [17] M. Chandramouli, B. Schoening, J. Quittek, T. Dietz and B. Claise, “Power and Energy Monitoring MIB,” ‘work in progress’ as IETF Internet Draft, May 2011. [18] A. Barnawi and R. Hafez, “A Time and Energy Efficiency Topology Discovery and Scheduling Protocol for Wireless Sensor Networks,” in Proc. of Int. Conf. on Computational Science and Engineering, Aug. 2009, pp. 570-578. [19] J. Furthm{\"u}ller and O. P. Waldhorst, “Energy-aware Resource Sharing with Mobile Devices,” in Proc. of Int. Conf. on Wireless On-Demand Network Systems and Services, Jan. 2011, pp. 52-59. [20] Dell, “Dell PowerEdge 6600 and PowerEdge 6650 Servers”; Available: www.dell.com/downloads/global/products/pedge/en/66x0_specs.pdf. [21] HP, “HP Rack-optimised Servers Build your Always-on Infrastructure”; Available: http://www.spectra.com/pdfs/rp5430_rp5470.pdf. [22] IBM, “pSeries 640 Model B80”; Available: http://www- 03.ibm.com/systems/power/hardware/pseries/entry/p640/index.html. [23] NS-2, The Network Simulator; Available: http://www.isi.edu/nsnam/ns/.",
year = "2012",
month = "12",
day = "31",
language = "English",
booktitle = "Unknown Host Publication",

}

Peoples, C, Parr, G, McClean, S, Scotney, BW, Morrow, PJ, Chaudhari, S & Theja, R 2012, An Energy Aware Network Management Approach using Server Profiling in 'Green' Clouds. in Unknown Host Publication. IEEE Second Symposium on Network Cloud Computing and Applications, 31/12/12.

An Energy Aware Network Management Approach using Server Profiling in 'Green' Clouds. / Peoples, Cathryn; Parr, Gerard; McClean, Sally; Scotney, BW; Morrow, PJ; Chaudhari, Santosh; Theja, Ravi.

Unknown Host Publication. 2012.

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

TY - GEN

T1 - An Energy Aware Network Management Approach using Server Profiling in 'Green' Clouds

AU - Peoples, Cathryn

AU - Parr, Gerard

AU - McClean, Sally

AU - Scotney, BW

AU - Morrow, PJ

AU - Chaudhari, Santosh

AU - Theja, Ravi

N1 - Reference text: [1] A. Mehta, M. Menaria, S. Dangi and S. Rao, “Energy Conservation in Cloud Infrastructures,” in Proc. of IEEE Int. Systems Conf., Apr. 2011, pp. 456-460. [2] U. Lee, I. Rimac, D. Kilper and V. Hilt, “Toward Energy-Efficient Content Dissemination,” in IEEE Network, Mar/Apr. 2011, Vol. 25, Iss. 2, pp. 14-19. [3] HM Treasury, “Autumn Statement 2011,” Nov. 2011; Available: www.hm-treasury.gov.uk. [4] Government of India, “Key Features of Budget 2012-2013,” Mar. 2012; Available: indiabudget.nic.in. [5] M. Adachi, T. Hiraoka and N. Komatsu, “A Study on a Resource Allocation Algorithm for On-demand Data Center Services,” in Proc. of Int. Conf. on Advanced Comm. Technology, Feb. 2008, pp. 295-300. [6] Opnet, “Opnet Modeler Accelerating Network R&D (Network Simulation)”; Available: http://www.opnet.com/. [7] C. Peoples, G. Parr and S. McClean, “Context-aware Characterisation of Energy Consumption in Data Centres,” in Proc. of IEEE Int. Workshop on Management of the Future Internet, May 2011, pp. 1250-1257. [8] C. Peoples, G. Parr and S. McClean, “Energy-aware Data Centre Management,” in Proc. of National Conference on Communications, Jan. 2011, pp. 1-5. [9] Hewlett-Packard Corporation, Intel Corporation, Microsoft Corporation, Phoenix Technologies Limited, and Toshiba Corporation, “Advanced Configuration and Power Interface Specification,” Revision 4.0a,. 2010. [10] Distributed Management Task Force; Available: www.dmtf.org. [11] Distributed Management Task Force, “CIM Schema,” Feb. 2011. [12] Intel, “Intel Intelligent Power Node Manager”; Available: www.intel.com/technology/nodemanager/. [13] Intel, “Intel Data Center Manager”; Available: software.intel.com/. [14] Intel, “Data Center Energy Efficiency with Intel Power Management Technologies”; Available: http://software.intel.com/. [15] IBM, “IBM PowerExecutive”; Available: www-03.ibm.com. [16] J. Parello and B. Claise, “Energy-aware Networks and Devices MIB,” ‘work in progress’ as IETF Internet Draft, Oct. 2011. [17] M. Chandramouli, B. Schoening, J. Quittek, T. Dietz and B. Claise, “Power and Energy Monitoring MIB,” ‘work in progress’ as IETF Internet Draft, May 2011. [18] A. Barnawi and R. Hafez, “A Time and Energy Efficiency Topology Discovery and Scheduling Protocol for Wireless Sensor Networks,” in Proc. of Int. Conf. on Computational Science and Engineering, Aug. 2009, pp. 570-578. [19] J. Furthmüller and O. P. Waldhorst, “Energy-aware Resource Sharing with Mobile Devices,” in Proc. of Int. Conf. on Wireless On-Demand Network Systems and Services, Jan. 2011, pp. 52-59. [20] Dell, “Dell PowerEdge 6600 and PowerEdge 6650 Servers”; Available: www.dell.com/downloads/global/products/pedge/en/66x0_specs.pdf. [21] HP, “HP Rack-optimised Servers Build your Always-on Infrastructure”; Available: http://www.spectra.com/pdfs/rp5430_rp5470.pdf. [22] IBM, “pSeries 640 Model B80”; Available: http://www- 03.ibm.com/systems/power/hardware/pseries/entry/p640/index.html. [23] NS-2, The Network Simulator; Available: http://www.isi.edu/nsnam/ns/.

PY - 2012/12/31

Y1 - 2012/12/31

N2 - Clouds and data centres are significant consumers of power. There are however, opportunities for optimising carbon cost here as resource redundancy is provisioned extensively. Data centre resources, and subsequently clouds which support them, are traditionally organised into tiers; switch-off activity when managing redundant resources therefore occurs in an approach which exploits cost advantages associated with closing down entire network portions. We suggest however, an alternative approach to optimise cloud operation while maintaining application QoS: Simulation experiments identify that network operation can be optimised by selecting servers which process traffic at a rate that more closely matches the packet arrival rate, and resources which provision excessive capacity additional to that required may be powered off for improved efficiency. This recognises that there is a point in server speed at which performance is optimised, and operation which is greater than or less than this rate will not achieve optimisation. A series of policies have been defined in this work for integration into cloud management procedures; performance results from their implementation and evaluation in simulation show improved efficiency by selecting servers based on these relationships.

AB - Clouds and data centres are significant consumers of power. There are however, opportunities for optimising carbon cost here as resource redundancy is provisioned extensively. Data centre resources, and subsequently clouds which support them, are traditionally organised into tiers; switch-off activity when managing redundant resources therefore occurs in an approach which exploits cost advantages associated with closing down entire network portions. We suggest however, an alternative approach to optimise cloud operation while maintaining application QoS: Simulation experiments identify that network operation can be optimised by selecting servers which process traffic at a rate that more closely matches the packet arrival rate, and resources which provision excessive capacity additional to that required may be powered off for improved efficiency. This recognises that there is a point in server speed at which performance is optimised, and operation which is greater than or less than this rate will not achieve optimisation. A series of policies have been defined in this work for integration into cloud management procedures; performance results from their implementation and evaluation in simulation show improved efficiency by selecting servers based on these relationships.

KW - autonomic content distribution

KW - cloud data centre

KW - context awareness

KW - dynamic configuration

KW - energy tolerance

KW - policy-based management

KW - NS-2

KW - Opnet

KW - self-managing platform.

M3 - Conference contribution

BT - Unknown Host Publication

ER -