Pattern Matching for Self- Tuning of MapReduce Jobs

TL;DR

This paper proposes a method to optimize MapReduce job execution by matching CPU utilization patterns using Dynamic Time Warping, enabling system parameter tuning based on historical application patterns.

Contribution

It introduces a pattern matching approach using DTW for self-tuning MapReduce jobs based on CPU utilization patterns, improving execution efficiency.

Findings

Effective pattern matching for CPU utilization using DTW.

Successful application to real MapReduce tasks.

Promising results on pseudo-distributed platforms.

Abstract

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a correlation analysis is then applied to DTWs outcomes to produce feasible similarity patterns. Three real applications (WordCount, Exim Mainlog parsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms.