Data centre energy costs are reduced when virtualisation is used as opposed to physical resource deployment to a degree sufficient to accommodate all application requests.Nonetheless, regardless of the hardware provisioning approach, opportunities remain with regard to the way in which resources are made available and workload is scheduled, particularly for improved efficiency objectives. In previous work, we propose the e-CAB as an architecture which captures real-time network state in data centres for improved efficiency. In this paper, we extend the discussion with consideration of a server selection mechanism integrated into the e-CAB which takes into account server utilisation and operational cost attributes. We recognise that cost incurred at a server is a function of its hardware characteristics. The objective of our approach is therefore to pack workload intodevices, selected as a function of their cost to operate, to achieve (or as close to) the maximum recommended capacity utilisation in a cost-efficient manner and help to avoid instances where devices are under-utilised and management cost is incurredinefficiently. This is based on principles behind queuing theory and the relationship between packet arrival rate, service rate and response time, and recognises a similar exponential relationship between power cost and server utilisation to drive its intelligentselection for improved efficiency. There is a subsequent opportunity to power redundant devices off to exploit power savings through avoiding their management.
|Title of host publication||Unknown Host Publication|
|Number of pages||4|
|Publication status||Published (in print/issue) - 31 May 2013|
|Event||IFIP/IEEE Integrated Network Management Symposium - Ghent, Belgium|
Duration: 31 May 2013 → …
|Conference||IFIP/IEEE Integrated Network Management Symposium|
|Period||31/05/13 → …|
Bibliographical noteReference text:  IBM Corporation, “IBM’s Strategy for Dynamic Infrastructure,” 2008.
 Intel, “Data Center Energy Efficiency with Intel Power Management Technologies,” Intel Information Technology, Feb 2010; Available:
 BT, “A Realist’s Guide to Green Data Centers,” 2008.
 VMware, “Energy Efficiency, Build a Green IT Infrastructure with Virtualisation;” Available: www.vmware.com/solutions/green-it/.
 VMware, “VMware vSphere 4: The CPU Scheduler in VMware ESX 4,” Technical White Paper, 2009.
 D. Dyachuk and M. Mazzucco, “On Allocation Policies for Power and Performance,” Proc. IEEE/ACM Int. Conf. on Grid Computing, Oct. 2010, pp. 313-320; doi: 10.1109/GRID.2010.5697986.
 W. Huang, M. Allen-Ware, J. B. Carter, E. Elnozahy, H. Hamann, T. Keller, C. Lefurgy, J. Li, K. Rajamani, and J. Rubio, “TAPO: Thermal-Aware Power Optimisation Techniques for Servers and Data Centers,” in Proc. Of Int. Green Computing Conf. and W’shops, Jul. 2011, pp. 1-8; doi: 10.1109/IGCC.2011.6008610.
 V. Manral, “Benchmarking Power Usage of Networking Devices,” ‘work in progress’ as an Internet Draft, Jan. 2011.
 K. Xiong and H. Perros, “Service Performance and Analysis in Cloud Computing,” in Proc. World Conf. on Services, Jul. 2009, pp. 693-700.
 M. Karir, “Data Centre Reference Architectures,” IETF ‘work in progress’ Internet Draft, Oct. 2011.
 C. Peoples, G. Parr and S.McClean, “Context-aware Characterisation of Energy Consumption in Data Centres,” in Proc. of 3rd IFIP/IEEE Int. W’shop on M’ment of the Future Internet, May 2011, pp. 1250-57.
- Cloud data centre
- cost-benefit balance
- green policy-based management
- operational efficiency
- workload scheduling.