Block trace analysis and storage system optimization: a practical approach with MATLAB/Python tools
Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO patt...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Place of publication not identified]
Apress
[2018]
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484239285/?ar |
| Zusammenfassung: | Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques-together with specially designed IO scheduling and data migration algorithms-are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You'll Learn: Understand the fundamental factors of data storage system performance Master an essential analytical skill using block trace via various applications Distinguish how the IO pattern differs in the block level from the file level Know how the sequential HDFS request becomes "fragmented" in final storage devices Perform trace analysis tasks with a tool based on the MATLAB and Python platforms |
| Beschreibung: | Vendor-supplied metadata |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781484239285 1484239288 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-047609028 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228120613.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2018 xx |||||o 00| ||eng c | ||
| 020 | |a 9781484239285 |c electronic bk. |9 978-1-4842-3928-5 | ||
| 020 | |a 1484239288 |c electronic bk. |9 1-4842-3928-8 | ||
| 035 | |a (DE-627-1)047609028 | ||
| 035 | |a (DE-599)KEP047609028 | ||
| 035 | |a (ORHE)9781484239285 | ||
| 035 | |a (DE-627-1)047609028 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a COM |2 bisacsh | |
| 072 | 7 | |a UKN |2 bicssc | |
| 082 | 0 | |a 004.6 |2 23 | |
| 100 | 1 | |a Xu, Jun |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Block trace analysis and storage system optimization |b a practical approach with MATLAB/Python tools |c Jun Xu |
| 264 | 1 | |a [Place of publication not identified] |b Apress |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 Online-Ressource | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Vendor-supplied metadata | ||
| 520 | |a Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques-together with specially designed IO scheduling and data migration algorithms-are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You'll Learn: Understand the fundamental factors of data storage system performance Master an essential analytical skill using block trace via various applications Distinguish how the IO pattern differs in the block level from the file level Know how the sequential HDFS request becomes "fragmented" in final storage devices Perform trace analysis tasks with a tool based on the MATLAB and Python platforms | ||
| 630 | 2 | 0 | |a MATLAB |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Computer networks | |
| 650 | 2 | |a Computer Communication Networks | |
| 650 | 4 | |a MATLAB | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 650 | 4 | |a Réseaux d'ordinateurs | |
| 650 | 4 | |a Network hardware | |
| 650 | 4 | |a COMPUTERS ; Computer Literacy | |
| 650 | 4 | |a COMPUTERS ; Computer Science | |
| 650 | 4 | |a COMPUTERS ; Data Processing | |
| 650 | 4 | |a COMPUTERS ; Hardware ; General | |
| 650 | 4 | |a COMPUTERS ; Information Technology | |
| 650 | 4 | |a COMPUTERS ; Machine Theory | |
| 650 | 4 | |a COMPUTERS ; Reference | |
| 650 | 4 | |a Computer networks | |
| 650 | 4 | |a Python (Computer program language) | |
| 776 | 1 | |z 148423927X | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 148423927X |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781484239285/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-047609028 |
|---|---|
| _version_ | 1821494873277071360 |
| adam_text | |
| any_adam_object | |
| author | Xu, Jun |
| author_facet | Xu, Jun |
| author_role | aut |
| author_sort | Xu, Jun |
| author_variant | j x jx |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)047609028 (DE-599)KEP047609028 (ORHE)9781484239285 |
| dewey-full | 004.6 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004.6 |
| dewey-search | 004.6 |
| dewey-sort | 14.6 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04197cam a22006852 4500</leader><controlfield tag="001">ZDB-30-ORH-047609028</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228120613.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781484239285</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4842-3928-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1484239288</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4842-3928-8</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047609028</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP047609028</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781484239285</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047609028</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">UKN</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004.6</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xu, Jun</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Block trace analysis and storage system optimization</subfield><subfield code="b">a practical approach with MATLAB/Python tools</subfield><subfield code="c">Jun Xu</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Place of publication not identified]</subfield><subfield code="b">Apress</subfield><subfield code="c">[2018]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Vendor-supplied metadata</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques-together with specially designed IO scheduling and data migration algorithms-are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You'll Learn: Understand the fundamental factors of data storage system performance Master an essential analytical skill using block trace via various applications Distinguish how the IO pattern differs in the block level from the file level Know how the sequential HDFS request becomes "fragmented" in final storage devices Perform trace analysis tasks with a tool based on the MATLAB and Python platforms</subfield></datafield><datafield tag="630" ind1="2" ind2="0"><subfield code="a">MATLAB</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Computer networks</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Computer Communication Networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MATLAB</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Réseaux d'ordinateurs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network hardware</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Computer Literacy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Data Processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Hardware ; General</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Information Technology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Machine Theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS ; Reference</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">148423927X</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">148423927X</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781484239285/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-047609028 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:21:18Z |
| institution | BVB |
| isbn | 9781484239285 1484239288 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Apress |
| record_format | marc |
| spelling | Xu, Jun VerfasserIn aut Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools Jun Xu [Place of publication not identified] Apress [2018] ©2018 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vendor-supplied metadata Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques-together with specially designed IO scheduling and data migration algorithms-are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You'll Learn: Understand the fundamental factors of data storage system performance Master an essential analytical skill using block trace via various applications Distinguish how the IO pattern differs in the block level from the file level Know how the sequential HDFS request becomes "fragmented" in final storage devices Perform trace analysis tasks with a tool based on the MATLAB and Python platforms MATLAB Python (Computer program language) Computer networks Computer Communication Networks Python (Langage de programmation) Réseaux d'ordinateurs Network hardware COMPUTERS ; Computer Literacy COMPUTERS ; Computer Science COMPUTERS ; Data Processing COMPUTERS ; Hardware ; General COMPUTERS ; Information Technology COMPUTERS ; Machine Theory COMPUTERS ; Reference 148423927X Erscheint auch als Druck-Ausgabe 148423927X |
| spellingShingle | Xu, Jun Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools MATLAB Python (Computer program language) Computer networks Computer Communication Networks Python (Langage de programmation) Réseaux d'ordinateurs Network hardware COMPUTERS ; Computer Literacy COMPUTERS ; Computer Science COMPUTERS ; Data Processing COMPUTERS ; Hardware ; General COMPUTERS ; Information Technology COMPUTERS ; Machine Theory COMPUTERS ; Reference |
| title | Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools |
| title_auth | Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools |
| title_exact_search | Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools |
| title_full | Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools Jun Xu |
| title_fullStr | Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools Jun Xu |
| title_full_unstemmed | Block trace analysis and storage system optimization a practical approach with MATLAB/Python tools Jun Xu |
| title_short | Block trace analysis and storage system optimization |
| title_sort | block trace analysis and storage system optimization a practical approach with matlab python tools |
| title_sub | a practical approach with MATLAB/Python tools |
| topic | MATLAB Python (Computer program language) Computer networks Computer Communication Networks Python (Langage de programmation) Réseaux d'ordinateurs Network hardware COMPUTERS ; Computer Literacy COMPUTERS ; Computer Science COMPUTERS ; Data Processing COMPUTERS ; Hardware ; General COMPUTERS ; Information Technology COMPUTERS ; Machine Theory COMPUTERS ; Reference |
| topic_facet | MATLAB Python (Computer program language) Computer networks Computer Communication Networks Python (Langage de programmation) Réseaux d'ordinateurs Network hardware COMPUTERS ; Computer Literacy COMPUTERS ; Computer Science COMPUTERS ; Data Processing COMPUTERS ; Hardware ; General COMPUTERS ; Information Technology COMPUTERS ; Machine Theory COMPUTERS ; Reference |
| work_keys_str_mv | AT xujun blocktraceanalysisandstoragesystemoptimizationapracticalapproachwithmatlabpythontools |