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Comparison of Scheduling Used In Bıg Data Frameworks

BROWSE_DETAIL_CREATION_DATE: 09-08-2018

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_CREATORS: Al-Jumaili, Saif Abdulrahman Mohemmed (Author),

BROWSE_DETAIL_CONTRIBUTERS: Yazıcı, Ali (Advisor),

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Job scheduling, Scheduler algorithms in Big Data, Apache Hadoop job scheduler, Big Data job scheduler, Apache Flink scheduling 


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Big Data applications have grown to become one of the main ingredients in the current information technology sector, providing an opportunity for decision-makers to achieve best outcomes, for instance in commerce and business. However, the speed at which such data gathering occurs varies in storage, management, and processing, in all of which the traditional database systems cannot handle such tasks as massive data collection. Resource management and task scheduling play an essential role in Big Data processing. There are different classifications of schedulers that are based on their different features, effectiveness, performance, and so on. However, in this thesis we classify, compare and investigate the detailed information associated with several schedulers being employed in Big Data frameworks. Moreover, this thesis identifies the weakness and strengths in different use cases of these schedulers. Furthermore, the study examines scenarios for the suitability of use cases so as to determine in which case the individual scheduler has some weakness or useless. Thus, these issues we cover in this thesis are not studied in the existing studies.


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