A bipartite graph model for placement, scheduling and replication in data grids

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buir.advisorAykanat, Cevdet
dc.contributor.authorDal, Burcu
dc.date.accessioned2016-01-08T18:19:22Z
dc.date.available2016-01-08T18:19:22Z
dc.date.issued2012
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical references leaves 63-68.en_US
dc.description.abstractData grids provide geographically distributed resources for applications that generate and utilize large data sets. However, there are some issues that hinder to ensure fast access to data and low turnaround time for the jobs in data grids. To address these issues, several data replication and job scheduling strategies have been introduced to offer high data availability, low bandwidth consumption, and reduced turnaround time for grid systems. Multiple copies of existing data are maintained at different locations via data replication. Data replication strategies are broadly categorized as static and dynamic. In static replication strategies, replication is performed during the system design, and replica decisions are generally based on a cost model that includes data access costs, bandwidth characteristics and storage constraints of the grid system. In dynamic replication strategies, the replication operation is managed at runtime so that the system adapts to the changes in user request patterns dynamically. Job scheduling strategies fall under two main categories: online mode and batch mode. The online mode scheduler assigns tasks to sites as soon as they arrive. In the batch mode, the complete set of jobs are taken into account and scheduled at the same time by using all the grid information. In this thesis, we propose a bipartite graph model for tasks and files in the grid system, and then we partition this graph to obtain a data placement and job scheduling strategy. The obtained parts are further refined in order to be assigned to grid sites by using a KL-based heuristic that takes the bandwidth and hop information between sites into account. Replication is achieved by replicating a certain amount of most accessed files chosen prior to the partitioning process. Experimental results indicate that the increase in the partitioning quality reflects positively on the mapping quality. Morever, it is observed that the communication cost is notably decreased when the data replication is applied. Hence, our results show that by replicating a small amount of data files and placing files onto sites using bipartite graph model, we can obtain performance improvement for scheduling jobs compared to no replication.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityDal, Burcuen_US
dc.format.extentxiii, 68 leaves, illustrationsen_US
dc.identifier.itemidB133861
dc.identifier.urihttp://hdl.handle.net/11693/15493
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData Gridsen_US
dc.subjectBipartite Graphen_US
dc.subjectData Placementen_US
dc.subjectJob Schedulingen_US
dc.subjectData Replicationen_US
dc.subject.lccQA76.9.C58 D35 2012en_US
dc.subject.lcshComputational grids (Computer systems)en_US
dc.subject.lcshGraph theory.en_US
dc.subject.lcshElectronic data processing--Backup processing alternatives.en_US
dc.subject.lcshData recovery (Computer science)en_US
dc.titleA bipartite graph model for placement, scheduling and replication in data gridsen_US
dc.typeThesisen_US

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