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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorAboulnaga, Ashraf-
dc.contributor.authorEad, Mostafa-
dc.contributor.authorBabu, Shivnath-
dc.contributor.authorHerodotou, Herodotos-
dc.date.accessioned2019-05-31T07:31:09Z-
dc.date.available2019-05-31T07:31:09Z-
dc.date.issued2014-01-01-
dc.identifier.citationAdvances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedingsen_US
dc.identifier.isbn978-389318065-3-
dc.description.abstractThe MapReduce programming model has become widely adopted for large scale analytics on big data. MapReduce systems such as Hadoop have many tuning parameters, many of which have a significant impact on performance. The map and reduce functions that make up a MapReduce job are developed using arbitrary programming constructs, which make them black-box in nature and therefore renders it difficult for users and administrators to make good parameter tuning decisions for a submitted MapReduce job. An approach that is gaining popularity is to provide automatic tuning decisions for submitted MapReduce jobs based on feedback from previously executed jobs. This approach is adopted, for example, by the Starfish system. Starfish and similar systems base their tuning decisions on an execution profile of the MapReduce job being tuned. This execution profile contains summary information about the runtime behavior of the job being tuned, and it is assumed to come from a previous execution of the same job. Managing these execution profiles has not been previously studied. This paper presents PStorM, a profile store and matcher that accurately chooses the relevant profiling information for tuning a submitted MapReduce job from the previously collected profiling information. PStorM can identify accurate tuning profiles even for previously unseen MapReduce jobs. PStorM is currently integrated with the Starfish system, although it can be extended to work with any MapReduce tuning system. Experiments on a large number of MapReduce jobs demonstrate the accuracy and efficiency of profile matching. The results of these experiments show that the profiles returned by PStorM result in tuning decisions that are as good as decisions based on exact profiles collected during pervious executions of the tuned jobs. This holds even for previously unseen jobs, which significantly reduces the overhead of feedback-driven profile-based MapReduce tuning.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© Copyright is with the authorsen_US
dc.titlePStorM: Profile storage and matching for feedback-based tuning of MapReduce jobsen_US
dc.typeConference Papersen_US
dc.collaborationAmazonen_US
dc.collaborationQatar Computing Research Instituteen_US
dc.collaborationMicrosoft Researchen_US
dc.collaborationUniversity of Waterlooen_US
dc.collaborationDuke Universityen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryUnited Statesen_US
dc.countryQataren_US
dc.countryCanadaen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceInternational Conference on Extending Database Technologyen_US
dc.identifier.doi10.5441/002/edbt.2014.02en_US
dc.identifier.scopus2-s2.0-84978739899en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84978739899en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyear2013-2014en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-8717-1691-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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