Profiling Memory Vulnerability of Big-data Applications
2016 (English)In: 2016 46TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W), IEEE, 2016, 258-261 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE, 2016. 258-261 p.
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-197019DOI: 10.1109/DSN-W.2016.58ISI: 000386564300050ScopusID: 2-s2.0-84994667675ISBN: 978-1-4673-8891-7OAI: oai:DiVA.org:kth-197019DiVA: diva2:1054808
46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), JUN 28-JUL 01, 2016, Toulouse, FRANCE
QC 201612092016-12-092016-11-282016-12-09Bibliographically approved