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引用次数: 20
摘要
重复数据删除技术是降低数据中心云备份业务对带宽和存储空间需求的理想解决方案。目前的重复数据删除解决方案是通过比较数据块的指纹(哈希值)来识别冗余数据,并将指纹存储在集中的服务器上。这种方法限制了大规模系统中的总体吞吐量和并发性能。此外,与硬盘相关的缓慢寻道时间降低了哈希查找操作(主要是随机I/ o)的性能。在本文中,我们提出了一个可扩展的混合哈希集群(SHHC)来维护一个低延迟的分布式哈希表,用于存储数据指纹。集群中的每个混合节点都由RAM和SSD (Solid State Drives)组成,以利用SSD固有的快速随机访问特性。这种分布式方法使系统具有可伸缩性,平衡了哈希存储上的负载,并显著减少了哈希查找过程的延迟。
SHHC: A Scalable Hybrid Hash Cluster for Cloud Backup Services in Data Centers
Data deduplication techniques are ideal solutions for reducing both bandwidth and storage space requirements for cloud backup services in data centers. Current data deduplication solutions rely on comparing fingerprints (hash values) of data chunks to identify redundant data and store the fingerprints on a centralized server. This approach limits the overall throughput and concurrency performance in large scale systems. Furthermore, the slow seek time associated with hard disks degrades the performance of hash lookup operations which are mainly random I/Os. In this paper we present a scalable hybrid hash cluster (SHHC) to maintain a low-latency distributed hash table for storing data fingerprints. Each hybrid node in the cluster is composed of RAM and Solid State Drives (SSD) to take advantage of the fast random access inherent in SSDs. This distributed approach makes the system scalable, balances the load on the hash store and significantly reduces the latency of the hash lookup process.