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Profiling Memory Vulnerability of Big-data Applications
KTH. Universitat Polit├Ęcnica de Catalunya, Spain.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
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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, Published paper (Refereed)
Abstract [en]

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.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-197019DOI: 10.1109/DSN-W.2016.58ISI: 000386564300050Scopus ID: 2-s2.0-84994667675ISBN: 978-1-4673-8891-7 (print)OAI: oai:DiVA.org:kth-197019DiVA: diva2:1054808
Conference
46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), JUN 28-JUL 01, 2016, Toulouse, FRANCE
Note

QC 20161209

Available from: 2016-12-09 Created: 2016-11-28 Last updated: 2016-12-09Bibliographically approved

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