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AuthorZhang, Fan
AuthorMalluhi, Qutaibah. M.
AuthorElsyed, Tamer M.
Available date2024-07-17T07:14:47Z
Publication Date2013
Publication NameProceedings of the International Conference on Parallel Processing
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICPP.2013.134
ISSN1903918
URIhttp://hdl.handle.net/10576/56760
AbstractThe rapid growth of large-data processing has brought in the MapReduce programming model as a widely accepted solution. However, MapReduce limits itself to a onemap- To-one-reduce framework. Meanwhile, it lacks built-in support and optimization when the input datasets are shared among concurrent applications and/or jobs. The performance might be improved when the shared and frequently accessed data is read from local instead of distributed file system. To enhance the performance of big data applications, this paper presents Concurrent MapReduce, a new programming model built on top of MapReduce that deals with large amount of shared data items. Concurrent MapReduce provides support for processing heterogeneous sources of input datasets and offers optimization when the datasets are partially or fully shared. Experimental evaluation has shown an execution runtime speedup of 4X compared to traditional nonconcurrent MapReduce implementation with a manageable time overhead.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectConcurrency
MapReduce
Programming model
Shared input data
TitleConMR: Concurrent MapReduce programming model for large scale shared-Data applications
TypeConference Paper
Pagination671-679
dc.accessType Abstract Only


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