Performance-power exploration of software-defined big data analytics: The AEGLE cloud backend

In this paper, we present the design and analyze the performance-energy characteristics of a software-defined infrastructure targeting Big Data analytics workloads. This software-defined Big Data framework forms the data analytic platform adopted in AEGLE¹, an European H2020 funded project for healthcare analytics. The developed framework utilizes state-of-art open source solutions and it is very flexible to enable the definition and automatic deployment of differing SPARK over Hadoop cluster configurations as analytics engines. In this paper, we exploit this flexibility of our software defined infrastructure to explore the performance-energy trade-offs of Big Data analytics under variable resource allocation scenarios. Specifically, we show that with respect to our local infrastructure, i.e., two Intel Xeon E5-2658A servers with 128GB RAM each, virtual cluster configurations with many nodes achieve the highest performance, while virtual cluster with high available RAM memory are more power efficient, exhibiting higher instructions per cycle (IPC) per kilojoule values.