dc.contributor.advisor | Quadt, Arnulf Prof. Dr. | |
dc.contributor.author | Magradze, Erekle | |
dc.date.accessioned | 2016-02-26T08:54:13Z | |
dc.date.available | 2016-02-26T08:54:13Z | |
dc.date.issued | 2016-02-26 | |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-0028-86DB-0 | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-5468 | |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject.ddc | 530 | de |
dc.title | Monitoring and Optimization of ATLAS Tier 2 Center GoeGrid | de |
dc.type | doctoralThesis | de |
dc.contributor.referee | Quadt, Arnulf Prof. Dr. | |
dc.date.examination | 2016-01-11 | |
dc.subject.gok | Physik (PPN621336750) | de |
dc.description.abstracteng | The demand on computational and storage resources is growing along with the amount of infor-
mation that needs to be processed and preserved. In order to ease the provisioning of the digital
services to the growing number of consumers, more and more distributed computing systems and
platforms are actively developed and employed. The building block of the distributed computing
infrastructure are single computing centers, similar to the Worldwide LHC Computing Grid, Tier
2 centre GoeGrid. The main motivation of this thesis was the optimization of GoeGrid perfor-
mance by efficient monitoring. The goal has been achieved by means of the GoeGrid monitoring
information analysis. The data analysis approach was based on the adaptive-network-based
fuzzy inference system (ANFIS) and machine learning algorithm such as Linear Support Vector
Machine (SVM).
The main object of the research was the digital service, since availability, reliability and ser-
viceability of the computing platform can be measured according to the constant and stable
provisioning of the services. Due to the widely used concept of the service oriented architecture
(SOA) for large computing facilities, in advance knowing of the service state as well as the quick
and accurate detection of its disability allows to perform the proactive management of the com-
puting facility. The proactive management is considered as a core component of the computing
facility management automation concept, such as Autonomic Computing. Thus in time as well
as in advance and accurate identification of the provided service status can be considered as a
contribution to the computing facility management automation, which is directly related to the
provisioning of the stable and reliable computing resources.
Based on the case studies, performed using the GoeGrid monitoring data, consideration of the
approaches as generalized methods for the accurate and fast identification and prediction of the
service status is reasonable. Simplicity and low consumption of the computing resources allow
to consider the methods in the scope of the Autonomic Computing component. | de |
dc.contributor.coReferee | Yahyapour, Ramin Prof. Dr. | |
dc.subject.eng | ATLAS Experiment, Grid Computing, Machine Learning, Service Status Identification, Service Status Prediction, Automation | de |
dc.identifier.urn | urn:nbn:de:gbv:7-11858/00-1735-0000-0028-86DB-0-8 | |
dc.affiliation.institute | Fakultät für Physik | de |
dc.identifier.ppn | 848535367 | |