@Article{cmc.2020.04604,
AUTHOR = {Yinghang Jiang, Qi Liu, Williams Dannah, Dandan Jin, Xiaodong Liu, Mingxu Sun},
TITLE = {An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {2},
PAGES = {713--729},
URL = {http://www.techscience.com/cmc/v62n2/38272},
ISSN = {1546-2226},
ABSTRACT = {Hadoop is a well-known parallel computing system for distributed computing 
and large-scale data processes. “Straggling” tasks, however, have a serious impact on task 
allocation and scheduling in a Hadoop system. Speculative Execution (SE) is an efficient 
method of processing “Straggling” Tasks by monitoring real-time running status of tasks 
and then selectively backing up “Stragglers” in another node to increase the chance to 
complete the entire mission early. Present speculative execution strategies meet challenges 
on misjudgement of “Straggling” tasks and improper selection of backup nodes, which 
leads to inefficient implementation of speculative executive processes. This paper has 
proposed an Optimized Resource Scheduling strategy for Speculative Execution (ORSE) 
by introducing non-cooperative game schemes. The ORSE transforms the resource 
scheduling of backup tasks into a multi-party non-cooperative game problem, where the 
tasks are regarded as game participants, whilst total task execution time of the entire cluster 
as the utility function. In that case, the most benefit strategy can be implemented in each 
computing node when the game reaches a Nash equilibrium point, i.e., the final resource 
scheduling scheme to be obtained. The strategy has been implemented in Hadoop-2.x.
Experimental results depict that the ORSE can maintain the efficiency of speculative 
executive processes and improve fault-tolerant and computation performance under the 
circumstances of Normal Load, Busy Load and Busy Load with Skewed Data.},
DOI = {10.32604/cmc.2020.04604}
}