Open Access

ARTICLE

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Yinghang Jiang¹, Qi Liu^2,3,*, Williams Dannah¹, Dandan Jin², Xiaodong Liu³, Mingxu Sun^4,*

1 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China.
2 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
3 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK.
4 School of Electrical Engineering, University of Jinan, China, and Centre for Health Sciences Research, University of Salford, Salford, Greater Manchester, M5 4WT, UK.

* Corresponding Authors: Qi Liu. Email: ;
   Mingxu Sun. Email: .

Computers, Materials & Continua 2020, 62(2), 713-729. https://doi.org/10.32604/cmc.2020.04604

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.

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