The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks

Kousiouris, G; Cucinotta, T; Varvarigou, T

dc.contributor.author	Kousiouris, G	en
dc.contributor.author	Cucinotta, T	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T01:37:19Z
dc.date.available	2014-03-01T01:37:19Z
dc.date.issued	2011	en
dc.identifier.issn	0164-1212	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21495
dc.subject	Artificial neural networks	en
dc.subject	Cloud computing	en
dc.subject	Genetic algorithms	en
dc.subject	Performance prediction	en
dc.subject	Real-time scheduling	en
dc.subject	Virtualization	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Black box method	en
dc.subject.other	Computing system	en
dc.subject.other	Critical parameter	en
dc.subject.other	Linear regression methods	en
dc.subject.other	Memory-sharing	en
dc.subject.other	Multicore architectures	en
dc.subject.other	Optimal allocation	en
dc.subject.other	Performance prediction	en
dc.subject.other	Physical nodes	en
dc.subject.other	Real time scheduling	en
dc.subject.other	Server consolidation	en
dc.subject.other	Virtual machines	en
dc.subject.other	Virtualizations	en
dc.subject.other	Cloud computing	en
dc.subject.other	Computer simulation	en
dc.subject.other	Couplings	en
dc.subject.other	Forecasting	en
dc.subject.other	Memory architecture	en
dc.subject.other	Optimization	en
dc.subject.other	Real time systems	en
dc.subject.other	Neural networks	en
dc.title	The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jss.2011.04.013	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jss.2011.04.013	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	The aim of this paper is to study and predict the effect of a number of critical parameters on the performance of virtual machines (VMs). These parameters include allocation percentages, real-time scheduling decisions and co-placement of VMs when these are deployed concurrently on the same physical node, as dictated by the server consolidation trend and the recent advances in the Cloud computing systems. Different combinations of VM workload types are investigated in relation to the aforementioned factors in order to find the optimal allocation strategies. What is more, different levels of memory sharing are applied, based on the coupling of VMs to cores on a multi-core architecture. For all the aforementioned cases, the effect on the score of specific benchmarks running inside the VMs is measured. Finally, a black box method based on genetically optimized artificial neural networks is inserted in order to investigate the degradation prediction ability a priori of the execution and is compared to the linear regression method. (C) 2011 Elsevier Inc. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE INC	en
heal.journalName	Journal of Systems and Software	en
dc.identifier.doi	10.1016/j.jss.2011.04.013	en
dc.identifier.isi	ISI:000292227600002	en
dc.identifier.volume	84	en
dc.identifier.issue	8	en
dc.identifier.spage	1270	en
dc.identifier.epage	1291	en