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
Who This Book Is For
IT professionals interested in storage system performance optimization:
network administrators, data storage managers, data storage engineers,
storage network engineers, systems engineers
