MapReduce: Limitations, optimizations and open issues
2013 (English)In: Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, IEEE , 2013, 1031-1038 p.Conference paper (Refereed)
MapReduce has recently gained great popularity as a programming model for processing and analyzing massive data sets and is extensively used by academia and industry. Several implementations of the MapReduce model have emerged, the Apache Hadoop framework being the most widely adopted. Hadoop offers various utilities, such as a distributed file system, job scheduling and resource management capabilities and a Java API for writing applications. Hadoop's success has intrigued research interest and has led to various modifications and extensions to the framework. Implemented optimizations include performance improvements, programming model extensions, tuning automation and usability enhancements. In this paper, we discuss the current state of the Hadoop framework and its identified limitations. We present, compare and classify Hadoop/MapReduce variations, identify trends, open issues and possible future directions.
Place, publisher, year, edition, pages
IEEE , 2013. 1031-1038 p.
, IEEE International Conference on Trust Security and Privacy in Computing and Communications, ISSN 2324-898X
Big Data, MapReduce, Survey
IdentifiersURN: urn:nbn:se:kth:diva-143846DOI: 10.1109/TrustCom.2013.126ISI: 000332856700131ScopusID: 2-s2.0-84893439928ISBN: 978-076955022-0OAI: oai:DiVA.org:kth-143846DiVA: diva2:712390
12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013; Melbourne, VIC; Australia; 16 July 2013 through 18 July 2013
QC 201404152014-04-152014-03-312014-06-05Bibliographically approved