Ontology type: schema:ScholarlyArticle
2013-08-09
AUTHORSYoungjae Kim, Aayush Gupta, Bhuvan Urgaonkar, Piotr Berman, Anand Sivasubramaniam
ABSTRACTEconomic forces, driven by the desire to introduce flash into the high-end storage market without changing existing software-base, have resulted in the emergence of solid-state drives (SSDs), flash packaged in HDD form factors and capable of working with device drivers and I/O buses designed for HDDs. Unlike the use of DRAM for caching or buffering, however, certain idiosyncrasies of NAND Flash-based solid-state drives (SSDs) make their integration into hard disk drive (HDD)-based storage systems nontrivial. Flash memory suffers from limits on its reliability, is an order of magnitude more expensive than the magnetic hard disk drives (HDDs), and can sometimes be as slow as the HDD (due to excessive garbage collection (GC) induced by high intensity of random writes). Given the complementary properties of HDDs and SSDs in terms of cost, performance, and lifetime, the current consensus among several storage experts is to view SSDs not as a replacement for HDD, but rather as a complementary device within the high-performance storage hierarchy. Thus, we design and evaluate such a hybrid storage system with HybridPlan that is an improved capacity planning technique to administrators with the overall goal of operating within cost-budgets. HybridPlan is able to find the most cost-effective hybrid storage configuration with different types of SSDs and HDDs More... »
PAGES277-303
http://scigraph.springernature.com/pub.10.1007/s11227-013-0999-3
DOIhttp://dx.doi.org/10.1007/s11227-013-0999-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1052358922
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0803",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Computer Software",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0805",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Distributed Computing",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA",
"id": "http://www.grid.ac/institutes/grid.135519.a",
"name": [
"National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA"
],
"type": "Organization"
},
"familyName": "Kim",
"givenName": "Youngjae",
"id": "sg:person.011437121775.21",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011437121775.21"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "IBM Almaden Research, San Jose, CA, USA",
"id": "http://www.grid.ac/institutes/grid.481551.c",
"name": [
"IBM Almaden Research, San Jose, CA, USA"
],
"type": "Organization"
},
"familyName": "Gupta",
"givenName": "Aayush",
"id": "sg:person.010254175725.29",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010254175725.29"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA",
"id": "http://www.grid.ac/institutes/grid.29857.31",
"name": [
"Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA"
],
"type": "Organization"
},
"familyName": "Urgaonkar",
"givenName": "Bhuvan",
"id": "sg:person.0730647150.39",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0730647150.39"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA",
"id": "http://www.grid.ac/institutes/grid.29857.31",
"name": [
"Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA"
],
"type": "Organization"
},
"familyName": "Berman",
"givenName": "Piotr",
"id": "sg:person.01274506210.27",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01274506210.27"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA",
"id": "http://www.grid.ac/institutes/grid.29857.31",
"name": [
"Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA"
],
"type": "Organization"
},
"familyName": "Sivasubramaniam",
"givenName": "Anand",
"id": "sg:person.01030534274.39",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01030534274.39"
],
"type": "Person"
}
],
"datePublished": "2013-08-09",
"datePublishedReg": "2013-08-09",
"description": "Economic forces, driven by the desire to introduce flash into the high-end storage market without changing existing software-base, have resulted in the emergence of solid-state drives (SSDs), flash packaged in HDD form factors and capable of working with device drivers and I/O buses designed for HDDs. Unlike the use of DRAM for caching or buffering, however, certain idiosyncrasies of NAND Flash-based solid-state drives (SSDs) make their integration into hard disk drive (HDD)-based storage systems nontrivial. Flash memory suffers from limits on its reliability, is an order of magnitude more expensive than the magnetic hard disk drives (HDDs), and can sometimes be as slow as the HDD (due to excessive garbage collection (GC) induced by high intensity of random writes). Given the complementary properties of HDDs and SSDs in terms of cost, performance, and lifetime, the current consensus among several storage experts is to view SSDs not as a replacement for HDD, but rather as a complementary device within the high-performance storage hierarchy. Thus, we design and evaluate such a hybrid storage system with HybridPlan that is an improved capacity planning technique to administrators with the overall goal of operating within cost-budgets. HybridPlan is able to find the most cost-effective hybrid storage configuration with different types of SSDs and HDDs",
"genre": "article",
"id": "sg:pub.10.1007/s11227-013-0999-3",
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.3089488",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1133522",
"issn": [
"0920-8542",
"1573-0484"
],
"name": "The Journal of Supercomputing",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "67"
}
],
"keywords": [
"solid-state drives",
"hard disk drives",
"capacity planning techniques",
"planning techniques",
"hybrid storage system",
"use of DRAM",
"disk drives",
"device drivers",
"storage hierarchy",
"storage system",
"magnetic hard disk drives",
"O bus",
"hybrid storage configuration",
"storage requirements",
"NAND flash",
"flash memory",
"storage experts",
"terms of cost",
"complementary properties",
"storage configuration",
"storage market",
"overall goal",
"system",
"DRAM",
"technique",
"requirements",
"certain idiosyncrasies",
"bus",
"different types",
"experts",
"drive",
"integration",
"hierarchy",
"administrators",
"complementary devices",
"performance",
"memory",
"cost",
"reliability",
"devices",
"goal",
"orders of magnitude",
"idiosyncrasies",
"buffering",
"order",
"flashes",
"drivers",
"lifetime",
"configuration",
"terms",
"use",
"market",
"form factors",
"emergence",
"consensus",
"types",
"desire",
"properties",
"economic forces",
"magnitude",
"factors",
"limit",
"force",
"replacement",
"current consensus"
],
"name": "HybridPlan: a capacity planning technique for projecting storage requirements in hybrid storage systems",
"pagination": "277-303",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1052358922"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s11227-013-0999-3"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s11227-013-0999-3",
"https://app.dimensions.ai/details/publication/pub.1052358922"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T17:00",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_594.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s11227-013-0999-3"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11227-013-0999-3'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11227-013-0999-3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11227-013-0999-3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11227-013-0999-3'
This table displays all metadata directly associated to this object as RDF triples.
162 TRIPLES
20 PREDICATES
90 URIs
81 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s11227-013-0999-3 | schema:about | anzsrc-for:08 |
2 | ″ | ″ | anzsrc-for:0803 |
3 | ″ | ″ | anzsrc-for:0805 |
4 | ″ | schema:author | N77c7f74ef82049beb6426c35c8e3cdc2 |
5 | ″ | schema:datePublished | 2013-08-09 |
6 | ″ | schema:datePublishedReg | 2013-08-09 |
7 | ″ | schema:description | Economic forces, driven by the desire to introduce flash into the high-end storage market without changing existing software-base, have resulted in the emergence of solid-state drives (SSDs), flash packaged in HDD form factors and capable of working with device drivers and I/O buses designed for HDDs. Unlike the use of DRAM for caching or buffering, however, certain idiosyncrasies of NAND Flash-based solid-state drives (SSDs) make their integration into hard disk drive (HDD)-based storage systems nontrivial. Flash memory suffers from limits on its reliability, is an order of magnitude more expensive than the magnetic hard disk drives (HDDs), and can sometimes be as slow as the HDD (due to excessive garbage collection (GC) induced by high intensity of random writes). Given the complementary properties of HDDs and SSDs in terms of cost, performance, and lifetime, the current consensus among several storage experts is to view SSDs not as a replacement for HDD, but rather as a complementary device within the high-performance storage hierarchy. Thus, we design and evaluate such a hybrid storage system with HybridPlan that is an improved capacity planning technique to administrators with the overall goal of operating within cost-budgets. HybridPlan is able to find the most cost-effective hybrid storage configuration with different types of SSDs and HDDs |
8 | ″ | schema:genre | article |
9 | ″ | schema:isAccessibleForFree | false |
10 | ″ | schema:isPartOf | N151aec5a8b184149bae240f5f168eb97 |
11 | ″ | ″ | N9e3f10ed3e85427ca51e4376338a2d8c |
12 | ″ | ″ | sg:journal.1133522 |
13 | ″ | schema:keywords | DRAM |
14 | ″ | ″ | NAND flash |
15 | ″ | ″ | O bus |
16 | ″ | ″ | administrators |
17 | ″ | ″ | buffering |
18 | ″ | ″ | bus |
19 | ″ | ″ | capacity planning techniques |
20 | ″ | ″ | certain idiosyncrasies |
21 | ″ | ″ | complementary devices |
22 | ″ | ″ | complementary properties |
23 | ″ | ″ | configuration |
24 | ″ | ″ | consensus |
25 | ″ | ″ | cost |
26 | ″ | ″ | current consensus |
27 | ″ | ″ | desire |
28 | ″ | ″ | device drivers |
29 | ″ | ″ | devices |
30 | ″ | ″ | different types |
31 | ″ | ″ | disk drives |
32 | ″ | ″ | drive |
33 | ″ | ″ | drivers |
34 | ″ | ″ | economic forces |
35 | ″ | ″ | emergence |
36 | ″ | ″ | experts |
37 | ″ | ″ | factors |
38 | ″ | ″ | flash memory |
39 | ″ | ″ | flashes |
40 | ″ | ″ | force |
41 | ″ | ″ | form factors |
42 | ″ | ″ | goal |
43 | ″ | ″ | hard disk drives |
44 | ″ | ″ | hierarchy |
45 | ″ | ″ | hybrid storage configuration |
46 | ″ | ″ | hybrid storage system |
47 | ″ | ″ | idiosyncrasies |
48 | ″ | ″ | integration |
49 | ″ | ″ | lifetime |
50 | ″ | ″ | limit |
51 | ″ | ″ | magnetic hard disk drives |
52 | ″ | ″ | magnitude |
53 | ″ | ″ | market |
54 | ″ | ″ | memory |
55 | ″ | ″ | order |
56 | ″ | ″ | orders of magnitude |
57 | ″ | ″ | overall goal |
58 | ″ | ″ | performance |
59 | ″ | ″ | planning techniques |
60 | ″ | ″ | properties |
61 | ″ | ″ | reliability |
62 | ″ | ″ | replacement |
63 | ″ | ″ | requirements |
64 | ″ | ″ | solid-state drives |
65 | ″ | ″ | storage configuration |
66 | ″ | ″ | storage experts |
67 | ″ | ″ | storage hierarchy |
68 | ″ | ″ | storage market |
69 | ″ | ″ | storage requirements |
70 | ″ | ″ | storage system |
71 | ″ | ″ | system |
72 | ″ | ″ | technique |
73 | ″ | ″ | terms |
74 | ″ | ″ | terms of cost |
75 | ″ | ″ | types |
76 | ″ | ″ | use |
77 | ″ | ″ | use of DRAM |
78 | ″ | schema:name | HybridPlan: a capacity planning technique for projecting storage requirements in hybrid storage systems |
79 | ″ | schema:pagination | 277-303 |
80 | ″ | schema:productId | N56d66e0fcdd344218674aa0a593da83c |
81 | ″ | ″ | Na3be248a0cc54d7194b2682ccecbc25b |
82 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1052358922 |
83 | ″ | ″ | https://doi.org/10.1007/s11227-013-0999-3 |
84 | ″ | schema:sdDatePublished | 2022-08-04T17:00 |
85 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
86 | ″ | schema:sdPublisher | N2e53c516483443e482ff893442822000 |
87 | ″ | schema:url | https://doi.org/10.1007/s11227-013-0999-3 |
88 | ″ | sgo:license | sg:explorer/license/ |
89 | ″ | sgo:sdDataset | articles |
90 | ″ | rdf:type | schema:ScholarlyArticle |
91 | N05f4d0a9c3b9483dba7c281178b8e03f | rdf:first | sg:person.01274506210.27 |
92 | ″ | rdf:rest | N4cf77d448a894ca18f42b9160ccc008f |
93 | N151aec5a8b184149bae240f5f168eb97 | schema:volumeNumber | 67 |
94 | ″ | rdf:type | schema:PublicationVolume |
95 | N2e53c516483443e482ff893442822000 | schema:name | Springer Nature - SN SciGraph project |
96 | ″ | rdf:type | schema:Organization |
97 | N4cf77d448a894ca18f42b9160ccc008f | rdf:first | sg:person.01030534274.39 |
98 | ″ | rdf:rest | rdf:nil |
99 | N56d66e0fcdd344218674aa0a593da83c | schema:name | doi |
100 | ″ | schema:value | 10.1007/s11227-013-0999-3 |
101 | ″ | rdf:type | schema:PropertyValue |
102 | N77c7f74ef82049beb6426c35c8e3cdc2 | rdf:first | sg:person.011437121775.21 |
103 | ″ | rdf:rest | Ncc27da16242c48c5bee19316c36c6df6 |
104 | N9e3f10ed3e85427ca51e4376338a2d8c | schema:issueNumber | 1 |
105 | ″ | rdf:type | schema:PublicationIssue |
106 | Na3be248a0cc54d7194b2682ccecbc25b | schema:name | dimensions_id |
107 | ″ | schema:value | pub.1052358922 |
108 | ″ | rdf:type | schema:PropertyValue |
109 | Nbbc2ffe11e164c049fc578adea740699 | rdf:first | sg:person.0730647150.39 |
110 | ″ | rdf:rest | N05f4d0a9c3b9483dba7c281178b8e03f |
111 | Ncc27da16242c48c5bee19316c36c6df6 | rdf:first | sg:person.010254175725.29 |
112 | ″ | rdf:rest | Nbbc2ffe11e164c049fc578adea740699 |
113 | anzsrc-for:08 | schema:inDefinedTermSet | anzsrc-for: |
114 | ″ | schema:name | Information and Computing Sciences |
115 | ″ | rdf:type | schema:DefinedTerm |
116 | anzsrc-for:0803 | schema:inDefinedTermSet | anzsrc-for: |
117 | ″ | schema:name | Computer Software |
118 | ″ | rdf:type | schema:DefinedTerm |
119 | anzsrc-for:0805 | schema:inDefinedTermSet | anzsrc-for: |
120 | ″ | schema:name | Distributed Computing |
121 | ″ | rdf:type | schema:DefinedTerm |
122 | sg:grant.3089488 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s11227-013-0999-3 |
123 | ″ | rdf:type | schema:MonetaryGrant |
124 | sg:journal.1133522 | schema:issn | 0920-8542 |
125 | ″ | ″ | 1573-0484 |
126 | ″ | schema:name | The Journal of Supercomputing |
127 | ″ | schema:publisher | Springer Nature |
128 | ″ | rdf:type | schema:Periodical |
129 | sg:person.010254175725.29 | schema:affiliation | grid-institutes:grid.481551.c |
130 | ″ | schema:familyName | Gupta |
131 | ″ | schema:givenName | Aayush |
132 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010254175725.29 |
133 | ″ | rdf:type | schema:Person |
134 | sg:person.01030534274.39 | schema:affiliation | grid-institutes:grid.29857.31 |
135 | ″ | schema:familyName | Sivasubramaniam |
136 | ″ | schema:givenName | Anand |
137 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01030534274.39 |
138 | ″ | rdf:type | schema:Person |
139 | sg:person.011437121775.21 | schema:affiliation | grid-institutes:grid.135519.a |
140 | ″ | schema:familyName | Kim |
141 | ″ | schema:givenName | Youngjae |
142 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011437121775.21 |
143 | ″ | rdf:type | schema:Person |
144 | sg:person.01274506210.27 | schema:affiliation | grid-institutes:grid.29857.31 |
145 | ″ | schema:familyName | Berman |
146 | ″ | schema:givenName | Piotr |
147 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01274506210.27 |
148 | ″ | rdf:type | schema:Person |
149 | sg:person.0730647150.39 | schema:affiliation | grid-institutes:grid.29857.31 |
150 | ″ | schema:familyName | Urgaonkar |
151 | ″ | schema:givenName | Bhuvan |
152 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0730647150.39 |
153 | ″ | rdf:type | schema:Person |
154 | grid-institutes:grid.135519.a | schema:alternateName | National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA |
155 | ″ | schema:name | National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA |
156 | ″ | rdf:type | schema:Organization |
157 | grid-institutes:grid.29857.31 | schema:alternateName | Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA |
158 | ″ | schema:name | Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA, USA |
159 | ″ | rdf:type | schema:Organization |
160 | grid-institutes:grid.481551.c | schema:alternateName | IBM Almaden Research, San Jose, CA, USA |
161 | ″ | schema:name | IBM Almaden Research, San Jose, CA, USA |
162 | ″ | rdf:type | schema:Organization |