| 期刊全称 | Block Trace Analysis and Storage System Optimization | | 期刊简称 | A Practical Approach | | 影响因子2023 | Jun Xu | | 视频video | | | 发行地址 | Brings together IO properties and metrics, and trace parsing and result reporting perspectives, based on the MATLAB and Python platforms.Introduces an open source tool that provides a powerful one-cli | | 图书封面 |  | | 影响因子 | 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 | | Pindex | Book 2018 |
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发表于 2025-3-21 18:30:35
