Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models (bibtex)
@article{Bacigalupo2011,
	_day = {17},
	_month = {09},
	abstract = {The automatic allocation of enterprise workload to resources can be enhanced by being able to make what--if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: (i) comparatively evaluate the layered queuing and historical techniques; (ii) evaluate the effectiveness of the management algorithm in different operating scenarios; and (iii) provide guidance on using prediction-based workload and resource management.},
	author = {D.A. Bacigalupo and van Hemert, J.I. and X. Chen and A. Usmani and A.P. Chester and L. He and D.N. Dillenberger and G.B. Wills and L. Gilbert and S.A. Jarvis},
	bibsource = {DBLP, http://dblp.uni-trier.de},
	date-added = {2011-09-17 20:22:22 +0100},
	date-modified = {2016-03-18 11:42:39 +0000},
	ee = {http://dx.doi.org/10.1016/j.simpat.2011.01.007},
	journal = {Simulation Modelling Practice and Theory},
	keywords = {e-science; scheduling},
	number = {6},
	pages = {1479--1495},
	title = {Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models},
	volume = {19},
	year = {2011}}
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