MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||5|
|Section||Modeling, Analysis, and Simulation of Intelligent Manufacturing Processes|
|Published online||19 June 2018|
An Improved Speculative Strategy for Heterogeneous Spark Cluster
Chongqing University of Posts and Telecommunications, 400065, Nan‘an District, Chongqing, P.R.China
2 Chongqing Mobile Internet Application Engineering Technology Research Center, 400065, Nan'an District, Chongqing, P.R.China
* Corresponding author : email@example.com
Apache Spark is an open-source in-memory cluster-computing framework. Spark decomposes an application into numerous tasks and assigns them to computing nodes for higher efficiency. However, in heterogeneous environments, some tasks become stragglers because of poor performance of some computing nodes, data skew, etc. These stragglers can affect cluster performance seriously since a job completes just when the last undertaking completions. To mitigate stragglers, Spark uses speculative execution which recognizes slow tasks and picks the node to run speculative task, but the low accuracy in identification and simple way of backing up will further extend the execution time. Then we develop an improved speculative strategy, DBMTPE (Data-Based Multiple Phases Time Estimation), which selects stragglers by estimating their remaining time and chooses a proper way to run speculative task according to the cause. Experiment results show that DBMTPE can run applications up to 10.5% faster over Spark-Native and save computing resource at the same time.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.