TY - GEN
T1 - Profiling Memory Vulnerability of Big-Data Applications
AU - Rameshan, N.
AU - Birke, R.
AU - Navarro, L.
AU - Vlassov, V.
AU - Urgaonkar, B.
AU - Kesidis, G.
AU - Schmatz, M.
AU - Chen, L. Y.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/22
Y1 - 2016/9/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84994667675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994667675&partnerID=8YFLogxK
U2 - 10.1109/DSN-W.2016.58
DO - 10.1109/DSN-W.2016.58
M3 - Conference contribution
AN - SCOPUS:84994667675
T3 - Proceedings - 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2016
SP - 258
EP - 261
BT - Proceedings - 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2016
Y2 - 28 June 2016 through 1 July 2016
ER -