Profiling Memory Vulnerability of Big-Data Applications

Navaneeth Rameshan, Robert Birke, Leandro Navarro, Vladimir Vlassov, Bhuvan Urgaonkar, George Kesidis, Martin L. Schmatz, Lydia Y. Chen

Abstract: Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set.