Storage Systems

Organization, Performance, Coding, Reliability, and Their Data

By: Alexander Thomasian

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Paperback | 20 October 2021 | Edition Number 1

At a Glance

Paperback
746 Pages

Dimensions(cm)
23.5 x 19.05 x 3.79

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Storage Systems: Organization, Performance, Coding, Reliability and Their Data Processing was motivated by the 1988 Redundant Array of Inexpensive/Independent Disks proposal to replace large form factor mainframe disks with an array of commodity disks. Disk loads are balanced by striping data into strips-with one strip per disk- and storage reliability is enhanced via replication or erasure coding, which at best dedicates k strips per stripe to tolerate k disk failures. Flash memories have resulted in a paradigm shift with Solid State Drives (SSDs) replacing Hard Disk Drives (HDDs) for high performance applications. RAID and Flash have resulted in the emergence of new storage companies, namely EMC, NetApp, SanDisk, and Purestorage, and a multibillion-dollar storage market. Key new conferences and publications are reviewed in this book.

The goal of the book is to expose students, researchers, and IT professionals to the more important developments in storage systems, while covering the evolution of storage technologies, traditional and novel databases, and novel sources of data. We describe several prototypes: FAWN at CMU, RAMCloud at Stanford, and Lightstore at MIT; Oracle's Exadata, AWS' Aurora, Alibaba's PolarDB, Fungible Data Center; and author's paper designs for cloud storage, namely heterogeneous disk arrays and hierarchical RAID.

Surveys storage technologies and lists sources of data: measurements, text, audio, images, and video
Familiarizes with paradigms to improve performance: caching, prefetching, log-structured file systems, and merge-trees (LSMs)
Describes RAID organizations and analyzes their performance and reliability
Conserves storage via data compression, deduplication, compaction, and secures data via encryption
Specifies implications of storage technologies on performance and power consumption
Exemplifies database parallelism for big data, analytics, deep learning via multicore CPUs, GPUs, FPGAs, and ASICs, e.g., Google's Tensor Processing Units

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