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.
Rameshan, N., Birke, R., Navarro, L., Vlassov, V., Urgaonkar, B., Kesidis, G., Schmatz, M., Chen, L. Profiling memory vulnerability of big-data applications. A: Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop. "2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W 2016): Toulouse, France: 28 June-1 July 2016". Toulouse: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 258-261.