A Study on Using Uncertain Time Series Matching Algorithms in MapReduce Applications

TL;DR

This paper explores using uncertain time series matching algorithms, specifically DTW, within MapReduce to predict application behavior and optimize system parameters based on CPU utilization patterns.

Contribution

It introduces a method combining DTW and statistical analysis to classify and predict CPU utilization patterns for MapReduce applications, enhancing execution efficiency.

Findings

Effective pattern matching with DTW for CPU utilization

Successful classification of applications based on patterns

Promising results on a 5-node MapReduce platform

Abstract

In this paper, we study CPU utilization time patterns of several Map-Reduce applications. After extracting running patterns of several applications, the patterns with their statistical information 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 along with its statistical information 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 statistical analysis is then applied to DTWs' outcomes to select the most suitable candidates. Moreover, under a hypothesis, another algorithm is proposed to classify applications under similar CPU utilization patterns. Three widely used text processing applications (WordCount, Distributed Grep, and Terasort) and another application (Exim Mainlog parsing) 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 5-node Map-Reduce platform