Show simple item record

AuthorKrzywda, Jakubdc.contributor.author
AuthorRezaie, Alidc.contributor.author
AuthorPapazachos, Zafeiriosdc.contributor.author
AuthorHamilton-Bryce, Ryandc.contributor.author
AuthorÖstberg, Per-Olovdc.contributor.author
AuthorAli-Eldin, Ahmeddc.contributor.author
AuthorMcCollum, Barrydc.contributor.author
AuthorDomaschka, Jörgdc.contributor.author
Date of accession2017-04-20T13:13:47Zdc.date.accessioned
Available in OPARU since2017-04-20T13:13:47Zdc.date.available
Year of creation2015-09-30dc.date.created
Date of first publication2017-04-20dc.date.issued
AbstractThis deliverable describes an enhanced version of the optimization model that features predictive capabilities. The purpose of this deliverable is to demonstrate how the enhanced model and advanced optimization algorithms support the optimization of a data center configuration. Predictive optimization capabilities of CactoOpt mainly support three optimization activities that can be performed on the logical (software) level of data center management: initial placement of virtual machines, migration of virtual machines, and vertical scaling. To deliver against these capabilities two software components were implemented: Workload Analysis and Classification Tool (WAC) and Application Behaviour Predictor. WAC is a tool that enables a cloud provider to deploy multiple auto-scaling algorithms suitable for different workload types. The tool assigns a workload to an auto-scaler based on the type of the workload, i.e., some auto-scalers can be better for bursty workloads while other auto-scalers can be better for workloads with strong patterns. The application behavior predictor is a tool that utilizes the knowledge about how the workload and the dynamics of the applications changes over time to predict the future state of the application for optimization purposes, e.g., how long will a task run before terminating on a given hardware configuration.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseCC BY-ND 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by-nd/4.0/dc.rights.uri
KeywordClouddc.subject
KeywordOptimisationdc.subject
KeywordSimulationdc.subject
KeywordAnalyticsdc.subject
KeywordCactos Projektdc.subject
KeywordContext-aware cloud topologydc.subject
KeywordData managementdc.subject
KeywordApplication modeldc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHCloud computingdc.subject.lcsh
LCSHElectric network topologydc.subject.lcsh
TitleExtended optimization model: project deliverable D3.3dc.title
Resource typeBerichtdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-4307dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-4346-6dc.identifier.urn
GNDCloud Computingdc.subject.gnd
GNDDatenmanagementdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Organisation und Management von Informationssystemenuulm.affiliationSpecific
DCMI TypeTextuulm.typeDCMI
TypeErstveröffentlichunguulm.veroeffentlichung
CategoryPublikationenuulm.category
In cooperation withQueen’s University of Belfastuulm.cooperation
In cooperation withUmeå Universitetuulm.cooperation
EU projectCACTOS / Context-Aware Cloud Topology Optimisation and Simulation / EC / FP7 / 610711uulm.projectEU
FundingEC / FP7uulm.funding


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC BY-ND 4.0 International
Except where otherwise noted, this item's license is described as CC BY-ND 4.0 International