An Experimental Evaluation of Giraph and Graphchi

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dc.contributor.authorJunnan, Lu
dc.contributor.supervisorThomo, Alex
dc.date.accessioned2016-08-29T17:44:28Z
dc.date.available2016-08-29T17:44:28Z
dc.date.copyright2016en_US
dc.date.issued2016-08-29
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractGraphs are the ultimate data structure to capture and represent the data of different connected entities. Graphs have become a very practical tool to model complicated relationships in various application domains, such as social media, protein, transportation, bibliographical, or knowledge networks. With the growth of popularity of cloud computing, graphs with millions of nodes and billion edges are becoming more common. Graph analytics is a critical component of big data discovery. The major problem in processing large graph data is the size and the irregular structure of the graph. In this report, we evaluate a Pregel implementation, Apache Giraph, on several algorithms. Also, we compare our results with a disk-based (centralized) system, GraphChi. We observe that for a moderate number of very simple machines, Giraph outperforms GraphChi for all the algorithms and datasets tested. This is in contrast to the claim of the GaphChi authors that one needs a cluster of more than 1,000 computers to perform comparably to GraphChi.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/7497
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ca/*
dc.subjectGiraphen_US
dc.subjectGraphchien_US
dc.subjectGraph Analyticsen_US
dc.subjectCloud Computingen_US
dc.subjectBig Dataen_US
dc.subjectPageranken_US
dc.titleAn Experimental Evaluation of Giraph and Graphchien_US
dc.typeprojecten_US

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