SZTS: A Novel Big Data Transportation System Benchmark Suite

Abstract

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose Shen Zhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as Hi Bench and Cloud Rank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at both the job and micro architecture level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and propose a methodology for identifying representative input data sets.