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Towards Memory-Optimized Data Shuffling Patterns for Big Data Analytics
به سوی الگوهای بر زدن داده ها حافظه بهینه شده برای تحلیل داده های بزرگ-2016 Big data analytics is an indispensable tool in
transforming science, engineering, medicine, healthcare, finance and ultimately business itself. With the explosion of
data sizes and need for shorter time-to-solution, in-memory
platforms such as Apache Spark gain increasing popularity.
However, this introduces important challenges, among which
data shuffling is particularly difficult: on one hand it is a
key part of the computation that has a major impact on
the overall performance and scalability so its efficiency is
paramount, while on the other hand it needs to operate with
scarce memory in order to leave as much memory available for
data caching. In this context, efficient scheduling of data transfers such that it addresses both dimensions of the problem
simultaneously is non-trivial. State-of-the-art solutions often
rely on simple approaches that yield sub-optimal performance
and resource usage. This paper contributes a novel shuffle data
transfer strategy that dynamically adapts to the computation
with minimal memory utilization, which we briefly underline
as a series of design principles.
Index Terms: big data analytics | data shuffling | memory efficient I/O | elastic buffering |
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