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
