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AuthorHauser, Christopher B.dc.contributor.author
Date of accession2017-06-30T09:12:25Zdc.date.accessioned
Available in OPARU since2017-06-30T09:12:25Zdc.date.available
Year of creation2016-09-29dc.date.created
Date of first publication2016-09-29dc.date.issued
AbstractThe idea of DisResc is to place workloads with different requirements on heterogeneous resources, while both the requirements and the resources are considered. The overall goals are to better utilise (heterogeneous) data centres, provide better user experiences by selecting the best matching hardware for a workload, and to allow different workload types like virtual machines, containers or HPC jobs side by side in one data centre. DisResc is the vision to build a cluster management software for cloud and hpc workloads, running in parallel on a heterogeneous physical data centre. DisResc enabled compute nodes should i) run virtual machines, containers or HPC jobs, ii) compile time-based behaviour profiles of their workloads, in order to iii) detect suboptimal situations like over/under utilization. The resource utilisation should consider multiple metrics like processor, memory, disk, and network utilisation. The cluster should communicate in a peer-based manner to agree on the best fitting placement of new workloads, and workloads of nodes in a suboptimal state. This non-centralised approach can remove single points for management and monitoring, which are usually found in Cloud and HPC clusters. Beside the distributed and stateful compute nodes, stateless gateway nodes allow user interactions with the cluster. The two main challenges and open questions of DisResc are first the compilation of a time-based behaviour profile out of monitoring data, and second to define a distributed consensus algorithm for determining a best fitting node for a behaviour profile.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordData centre managementdc.subject
KeywordVirtualizationdc.subject
KeywordContainersdc.subject
KeywordResource profilingdc.subject
KeywordWorkload schedulingdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHCloud computingdc.subject.lcsh
LCSHData curationdc.subject.lcsh
LCSHSchedulingdc.subject.lcsh
TitleDisResc: self-organized and flexible distributed resource clusterdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-4406dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-4445-5dc.identifier.urn
GNDCloud Computingdc.subject.gnd
GNDDatenmanagementdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Organisation und Management von Informationssystemenuulm.affiliationSpecific
Citation of original publ.Hauser, Christopher B. (2016): Self-organized and flexible distributed resource cluster: 'DisResc': portfolio with abstract and poster. In: Cloud Forward Conference 2016: From Distributed to Complete Computing, Madrid, 18-20 October, Madrid, 29.09.2016, http://cf2016.holacloud.eu/portfolio/self-organized-and-flexible-distributed-resource-cluster-disresc/ (Letzter Zugriff: 27.06.2017)uulm.citationOrigPub
Peer reviewjauulm.peerReview
DCMI TypeEventuulm.typeDCMI
TypeZweitveröffentlichunguulm.veroeffentlichung
CategoryPublikationenuulm.category
EU projectCACTOS / Context-Aware Cloud Topology Optimisation and Simulation / EC / FP7 / 610711uulm.projectEU
FundingEC / FP7uulm.funding
Conference nameCloud Forward Conference 2016: From Distributed to Complete Computinguulm.conferenceName
Conference placeMadriduulm.conferencePlace
Conference start date2016-10-18uulm.conferenceStartDate
Conference end date2016-10-20uulm.conferenceEndDate
University Bibliographyjauulm.unibibliographie


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