Implications of non-volatile memory as primary storage for database management systems
dc.citation.epage | 171 | en_US |
dc.citation.spage | 164 | en_US |
dc.contributor.author | Mustafa, Naveed Ul | en_US |
dc.contributor.author | Armejach, A. | en_US |
dc.contributor.author | Öztürk, Özcan | en_US |
dc.contributor.author | Cristal, A. | en_US |
dc.contributor.author | Unsal, O. S. | en_US |
dc.coverage.spatial | Agios Konstantinos, Greece | en_US |
dc.date.accessioned | 2018-04-12T11:45:30Z | |
dc.date.available | 2018-04-12T11:45:30Z | |
dc.date.issued | 2017 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description | Date of Conference: 17-21 July 2016 | en_US |
dc.description.abstract | Traditional 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.provenance | Made 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: 2017 | en |
dc.identifier.doi | 10.1109/SAMOS.2016.7818344 | en_US |
dc.identifier.isbn | 9781509030767 | |
dc.identifier.uri | http://hdl.handle.net/11693/37609 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/SAMOS.2016.7818344 | en_US |
dc.source.title | 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS) | en_US |
dc.subject | Computer architecture | en_US |
dc.subject | Data storage equipment | en_US |
dc.subject | Database systems | en_US |
dc.subject | Digital storage | en_US |
dc.subject | Dynamic random access storage | en_US |
dc.subject | Embedded systems | en_US |
dc.subject | Management information systems | en_US |
dc.subject | Memory architecture | en_US |
dc.subject | Nonvolatile storage | en_US |
dc.subject | Query processing | en_US |
dc.subject | Relational database systems | en_US |
dc.subject | Virtual storage | en_US |
dc.subject | Emulation platform | en_US |
dc.subject | Intermediate storage | en_US |
dc.subject | Non-volatile memory | en_US |
dc.subject | Power constraints | en_US |
dc.subject | Primary storages | en_US |
dc.subject | Query execution time | en_US |
dc.subject | Relational data | en_US |
dc.subject | Storage capacity | en_US |
dc.subject | Information management | en_US |
dc.title | Implications of non-volatile memory as primary storage for database management systems | en_US |
dc.type | Conference Paper | en_US |
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