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AutorByrne, Jamesdc.contributor.author
AutorSvorobej, Sergejdc.contributor.author
AutorCastañé, Gabriel Gonzálezdc.contributor.author
AutorStier, Christiandc.contributor.author
AutorKrach, Sebastiandc.contributor.author
AutorAli-Eldin, Ahmeddc.contributor.author
AutorKrzywda, Jakubdc.contributor.author
AutorByrne, Peter J.dc.contributor.author
In OPARU verfügbar seit2017-04-20T13:44:25Zdc.date.available
Jahr der Erstellung2016-09-30dc.date.created
Datum der Erstveröffentlichung2017-04-20dc.date.issued
ZusammenfassungSince the arrival of cloud computing, a significant amount of research has been and continues to be carried out towards the creation of efficient optimisation strategies for meeting certain optimisation goals such as energy efficiency, resource consolidation or performance improvement within virtualised data centres. However, investigating whether specific optimisation algorithms can achieve the desired function in a production environment, and investigating how well they operate are quite complex tasks. Untested optimisation rules typically cannot be directly deployed in the production system, instead requiring manual test-bed experiments. This technique can be prohibitively costly, time consuming and cannot always account for scale and other constraints. This work presents a design-time optimisation evaluation solution based on discrete event simulation for cloud computing. By using a simulation toolkit (CactoSim) coupled with a runtime optimisation toolkit (CactoOpt), a cloud architect is able to create a direct replica model of the data centre production environment and then run simulations which take into account optimisation strategies. Results produced by such simulations can be used to estimate the optimisation algorithm performance under various conditions. With CACTOS addressing the efficient management of IaaS data centres running Scientific Computing, Business Analytics and White-Box applications, the CACTOS Prediction Toolkit supports design time decision-making via simulation for each of these areas. Typical scenarios for each of the three use cases of scientific computing, business analytics and white box applications have been modelled, run and analysed using simulation, taking the optimisation algorithms into account, and these are presented in this document. This deliverable represents the final part of two iterative pieces of work.dc.description.abstract
Verbreitende StelleUniversität Ulmdc.publisher
LizenzCC BY-ND 4.0 Internationaldc.rights
Link zum Lizenztexthttps://creativecommons.org/licenses/by-nd/4.0/dc.rights.uri
SchlagwortData managementdc.subject
SchlagwortContext-aware cloud topologydc.subject
SchlagwortCloud servicesdc.subject
DDC-SachgruppeDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHCloud computingdc.subject.lcsh
LCSHElectric network topologydc.subject.lcsh
TitelFinal results from optimisation models validation and experimentation: project deliverable D6.5dc.title
GNDCloud Computingdc.subject.gnd
FakultätFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Organisation und Management von Informationssystemenuulm.affiliationSpecific
DCMI MedientypTextuulm.typeDCMI
Kooperation mitDublin City Universityuulm.cooperation
Kooperation mitFZI Forschungszentrum Informatik am Karlsruher Institut für Technologieuulm.cooperation
Kooperation mitUmeå Universitetuulm.cooperation
EU-ProjektCACTOS / Context-Aware Cloud Topology Optimisation and Simulation / EC / FP7 / 610711uulm.projectEU
FörderinformationenEC / FP7uulm.funding

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CC BY-ND 4.0 International
Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: CC BY-ND 4.0 International