Hadoop Workloads Characterization for Performance and Energy Efficiency Optimizations on Microservers

Abstract

The traditional low-power embedded processors such as Atom and ARM are entering into the high-performance server market. At the same time, big data analytics applications are emerging and dramatically changing the landscape of data center workloads. Emerging big data applications require a significant amount of server computational power. However, the rapid growth in the data yields challenges to process them efficiently using current high-performance server architectures. Furthermore, physical design constraints, such as power and density have become the dominant limiting factor for scaling out servers. Numerous big data applications rely on using Hadoop MapReduce framework to perform their analysis on large-scale datasets. Since Hadoop configuration parameters as well as system parameters directly affect the MapReduce job performance and energy-efficiency, joint application, system, and architecture level parameters tuning is vital to maximize the energy efficiency for Hadoop-based applications. In this work, through methodical investigation of performance and power measurements, we demonstrate how the interplay among various Hadoop configuration parameters, as well as system and architecture level parameters affect not only the performance but also the energy-efficiency across various big data applications. Our results identify trends to guide scheduling decision and key insights to help improving Hadoop MapReduce applications performance, power, and energy-efficiency on microservers.