Big Data Dwarfs: Towards Fully Understanding Big Data Analytics Workloads

02/01/2018

∙

by   Wanling Gao, et al.

∙

0

∙

share

Though the big data benchmark suites like BigDataBench and CloudSuite have been used in architecture and system researches, we have not yet answered the fundamental issue-- what are abstractions of frequently-appearing units of computation in big data analytics, which we call big data dwarfs. For the first time, we identify eight big data dwarfs, each of which captures the common requirements of each class of unit of computation while being reasonably divorced from individual implementations among a wide variety of big data analytics workloads. We implement the eight dwarfs on different software stacks as the dwarf components. We present the application of the big data dwarfs to construct big data proxy benchmarks using the directed acyclic graph (DAG)-like combinations of the dwarf components with different weights to mimic the benchmarks in BigDataBench. Our proxy benchmarks shorten the execution time by 100s times on the real systems while they are qualified for both earlier architecture design and later system evaluation across different architectures.