Profiling memory vulnerability of big-data applications

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/97424

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor	Universitat Politècnica de Catalunya. CNDS - Xarxes de Computadors i Sistemes Distribuïts
dc.contributor.author	Rameshan, Navaneeth
dc.contributor.author	Birke, R.
dc.contributor.author	Navarro Moldes, Leandro
dc.contributor.author	Vlassov, Vladimir
dc.contributor.author	Urgaonkar, B.
dc.contributor.author	Kesidis, G.
dc.contributor.author	Schmatz, M.
dc.contributor.author	Chen, L. Y.
dc.date	2016
dc.identifier.citation	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.
dc.identifier.citation	9781509036899
dc.identifier.citation	10.1109/DSN-W.2016.58
dc.identifier.uri	http://hdl.handle.net/2117/97424
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://ieeexplore.ieee.org.recursos.biblioteca.upc.edu/document/7575390/?reload=true
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
dc.subject	Cloud computing
dc.subject	Big data
dc.subject	Big data
dc.subject	Cache storage
dc.subject	Cloud computing
dc.subject	Data analysis
dc.subject	Profiling memory vulnerability
dc.subject	Cloud computing
dc.subject	In-memory big-data analytics
dc.subject	Memory subsystems
dc.subject	Cache bandwidth
dc.subject	Memory bandwidth
dc.subject	Spark applications
dc.subject	Computació en núvol
dc.subject	Macrodades
dc.title	Profiling memory vulnerability of big-data applications
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.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.
dc.description.abstract	Peer Reviewed

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