國立虎尾科技大學 |

Block Trace Analysis and Storage System Optimization = A Practical Approach with MATLAB/Python Tools /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Block Trace Analysis and Storage System Optimization/ by Jun Xu.
其他題名:	A Practical Approach with MATLAB/Python Tools /
作者:	Xu, Jun.
面頁冊數:	XVII, 271 p. 91 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computer communication systems. -
電子資源:	https://doi.org/10.1007/978-1-4842-3928-5
ISBN:	9781484239285

Block Trace Analysis and Storage System Optimization = A Practical Approach with MATLAB/Python Tools /
Xu, Jun.

Block Trace Analysis and Storage System OptimizationA Practical Approach with MATLAB/Python Tools /[electronic resource] :by Jun Xu. - 1st ed. 2018. - XVII, 271 p. 91 illus.online resource.

Chapter 1: Introduction -- Chapter 2: Trace Characteristics -- Chapter 3: Trace Collection -- Chapter 4: Trace Analysis -- Chapter 5: Case Study: Benchmarking Tools -- Chapter 6: Case Study: Modern Disks -- Chapter 7: Case Study: RAID -- Chapter 8: Case Study: Hadoop -- Chapter 9: Case Study: Ceph -- Appendix A: Tools and Functions -- Appendix B: Blktrace and 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 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.

ISBN: 9781484239285

Standard No.: 10.1007/978-1-4842-3928-5doiSubjects--Topical Terms:

1115394
Computer communication systems.

LC Class. No.: TK5105.5-5105.9

Dewey Class. No.: 004.6

Block Trace Analysis and Storage System Optimization = A Practical Approach with MATLAB/Python Tools /
LDR:03733nam a22003975i 4500 001 988024
003 DE-He213
005 20200706013715.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781484239285 $9 978-1-4842-3928-5
024 7 $a 10.1007/978-1-4842-3928-5 $2 doi
035 $a 978-1-4842-3928-5
050 4 $a TK5105.5-5105.9
072 7 $a UKN $2 bicssc
072 7 $a COM075000 $2 bisacsh
072 7 $a UKN $2 thema
082 0 4 $a 004.6 $2 23
100 1 $a Xu, Jun. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1029039
245 1 0 $a Block Trace Analysis and Storage System Optimization $h [electronic resource] : $b A Practical Approach with MATLAB/Python Tools / $c by Jun Xu.
250 $a 1st ed. 2018.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a XVII, 271 p. 91 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1: Introduction -- Chapter 2: Trace Characteristics -- Chapter 3: Trace Collection -- Chapter 4: Trace Analysis -- Chapter 5: Case Study: Benchmarking Tools -- Chapter 6: Case Study: Modern Disks -- Chapter 7: Case Study: RAID -- Chapter 8: Case Study: Hadoop -- Chapter 9: Case Study: Ceph -- Appendix A: Tools and Functions -- Appendix B: Blktrace and Tools.
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.
650 0 $a Computer communication systems. $3 1115394
650 1 4 $a Computer Communication Networks. $3 669310
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484239278
776 0 8 $i Printed edition: $z 9781484239292
776 0 8 $i Printed edition: $z 9781484248416
856 4 0 $u https://doi.org/10.1007/978-1-4842-3928-5
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)