@inproceedings{3a38720ea40b40849f0080740fc9f6d3,
title = "Profiling Memory Vulnerability of Big-Data Applications",
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.",
author = "N. Rameshan and R. Birke and L. Navarro and V. Vlassov and B. Urgaonkar and G. Kesidis and M. Schmatz and Chen, \{L. Y.\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 46th IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2016 ; Conference date: 28-06-2016 Through 01-07-2016",
year = "2016",
month = sep,
day = "22",
doi = "10.1109/DSN-W.2016.58",
language = "English (US)",
series = "Proceedings - 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "258--261",
booktitle = "Proceedings - 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2016",
address = "United States",
}