Implications of non-volatile memory as primary storage for database management systems

dc.citation.epage171en_US
dc.citation.spage164en_US
dc.contributor.authorMustafa, Naveed Ulen_US
dc.contributor.authorArmejach, A.en_US
dc.contributor.authorÖztürk, Özcanen_US
dc.contributor.authorCristal, A.en_US
dc.contributor.authorUnsal, O. S.en_US
dc.coverage.spatialAgios Konstantinos, Greeceen_US
dc.date.accessioned2018-04-12T11:45:30Z
dc.date.available2018-04-12T11:45:30Z
dc.date.issued2017en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 17-21 July 2016en_US
dc.description.abstractTraditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively. © 2016 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:45:30Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1109/SAMOS.2016.7818344en_US
dc.identifier.isbn9781509030767
dc.identifier.urihttp://hdl.handle.net/11693/37609
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SAMOS.2016.7818344en_US
dc.source.title2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS)en_US
dc.subjectComputer architectureen_US
dc.subjectData storage equipmenten_US
dc.subjectDatabase systemsen_US
dc.subjectDigital storageen_US
dc.subjectDynamic random access storageen_US
dc.subjectEmbedded systemsen_US
dc.subjectManagement information systemsen_US
dc.subjectMemory architectureen_US
dc.subjectNonvolatile storageen_US
dc.subjectQuery processingen_US
dc.subjectRelational database systemsen_US
dc.subjectVirtual storageen_US
dc.subjectEmulation platformen_US
dc.subjectIntermediate storageen_US
dc.subjectNon-volatile memoryen_US
dc.subjectPower constraintsen_US
dc.subjectPrimary storagesen_US
dc.subjectQuery execution timeen_US
dc.subjectRelational dataen_US
dc.subjectStorage capacityen_US
dc.subjectInformation managementen_US
dc.titleImplications of non-volatile memory as primary storage for database management systemsen_US
dc.typeConference Paperen_US

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