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This document explores the critical aspects of replica placement and data deduplication in storage systems. It discusses how to strategically place replicas across different physical locations to minimize risks, considering factors such as server crashes, rack failures, and natural disasters. Additionally, it addresses the importance of data deduplication as a method of improving storage utilization by eliminating redundant data through advanced techniques. The text also emphasizes the significance of read performance in deduplication storage, particularly regarding virtual machine images, and provides references for performance benchmarking tools.
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Replica Placement • Most existing works focus on how to place replica with low cost. • Maybe it is safer that we separate the replicas as far as possible? • In same server => server crash • In same rack = > rack failure • In same datacenter = > earthquake or other cataclysms • Consider both distance and cost.
Data Deduplication • Data deduplication is a specialized data compression technique for eliminating coarse-grained redundant data. • Improve storage utilization. • Issues: • How to improve the duplication detection and chuck existence querying efficiency. • Efficient chunking, faster hash indexing, locality-preserving index catching, and efficient bloom filters …etc. • Compressing the unique chunks and performing (fixed-size) large writes through containers or similar structures.
Read Performance of Deduplication Storage • Publication of David H. C. Du, HPCC’11. • Read performance is critical to reconstruct the original data stream.
Read Performance of Deduplication Storage(Cont.) • One example is to store images of VMs(process/memory/disk) to shared network storage. • VM images of idle desktops are migrated to network storage for energy saving.
Benchmarks • Filebench • http://sourceforge.net/apps/mediawiki/filebench/index.php • Phoronix Test Suite – disk test suite • http://www.phoronix-test-suite.com/ • Bonnie++ • http://freecode.com/projects/bonnie