We introduced a new computation model that reflects the assumptions of the map-reduce
framework, but allows for networks of processes other than Map feeding Reduce. We illustrated the
benefit of our model by showing how to improve the computation of the 3- way join and by
developing algorithms for merging and sorting. The cost measures by which we evaluate the algorithms
are the communication among processes and the processing time, both total over all processes and
elapsed (i.e., exploiting parallelism). The input file is hold on as a group of files on persistent storage.
The goal of a sorting system is to rework this input file into associate ordered set of output files,
conjointly hold on persistent storage, such the concatenation of those output files so as constitutes
the sorted version of the input file. Our goal is to style and implement a sorting system that may sort
datasets of the targeted size whereas achieving a good exchange between speed, resource utilization,
and cost.
Real Time Impact Factor:
Pending
Author Name: Ms. Neha Chaturvedi, Mrs. Richa Jain ,Mr. Anil Pimpalapure
URL: View PDF
Keywords: Big Data, Cloud Computing, Hadoop, Mapreduce, deep sort method
ISSN: 2455-6203
EISSN: 2455-6203
EOI/DOI:
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