IEEE/ACM 16th Intl. Symposium on Cluster, Cloud and Grid Computing, CCGrid 2016 — 2016
Towards Memory-Optimized Data Shuffling Patterns for Big Data Analytics
Bogdan Nicolae, Carlos H. A. Costa, Claudia Misale, Kostas Katrinis, Yoonho Park
Abstract
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.
@inproceedings{16:ccgrid:misale,
abstract = {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.},
address = {Cartagena, Colombia},
author = {Nicolae, Bogdan and Costa, Carlos H. A. and Misale, Claudia and Katrinis, Kostas and Park, Yoonho},
booktitle = {{IEEE/ACM} 16th Intl. Symposium on Cluster, Cloud and Grid Computing, CCGrid 2016},
date-modified = {2024-04-01 11:18:22 +0200},
doi = {10.1109/CCGrid.2016.85},
keywords = {HPC},
publisher = {IEEE},
title = {Towards Memory-Optimized Data Shuffling Patterns for Big Data Analytics},
url = {http://ieeexplore.ieee.org/document/7515716/},
year = {2016},
bdsk-url-1 = {http://ieeexplore.ieee.org/document/7515716/},
bdsk-url-2 = {http://dx.doi.org/10.1109/CCGrid.2016.85}}