Characterizing and Subsetting Big Data Workloads

TL;DR

This paper uses PCA and clustering to analyze and select representative workloads from BigDataBench, facilitating more efficient and diverse benchmarking of big data systems.

Contribution

It introduces a methodology combining PCA and clustering to characterize and subset big data workloads, improving benchmark diversity and efficiency.

Findings

Identified key workload characteristics using PCA

Clustered workloads based on principal components

Selected seven representative workloads for benchmarking

Abstract

Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates hese challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.