HybridPlan: a capacity planning technique for projecting storage requirements in hybrid storage systems View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-08-09

AUTHORS

Youngjae Kim, Aayush Gupta, Bhuvan Urgaonkar, Piotr Berman, Anand Sivasubramaniam

ABSTRACT

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 More... »

PAGES

277-303

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11227-013-0999-3

DOI

http://dx.doi.org/10.1007/s11227-013-0999-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052358922


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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

HOW TO GET THIS DATA PROGRAMMATICALLY:

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
 




Preview window. Press ESC to close (or click here)


...