最小化数据中心间键值存储中的广域网通信

H. Horie, M. Asahara, H. Yamada, K. Kono
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引用次数: 0

摘要

云联盟已经成为互联网规模服务的流行平台。云联合在多个数据中心上运行,因为云联合是在单个数据中心中运行的云服务的集合。在这种数据中心间环境中,分布式键值存储(dkv)在可伸缩性方面是很有吸引力的数据库。但是,数据中心间通信会降低这些dkvs的性能,因为它们的延迟大,带宽窄。在本文中,我们演示了如何减少和隐藏dkvs的数据中心间通信的弱点。为了解决这些问题,我们引入了两种技术,即多层DHT (ML-DHT)和本地优先数据重建(LDR)。ML-DHT提供全局一致的键值对索引,具有存储容量的高效可扩展性。它采用了一种路由协议,减少了通过数据中心间连接的路由跳数。LDR通过使用擦除编码技术减少数据中心间连接上的数据传输。它使KVS管理员能够灵活地在存储容量的可扩展性和数据传输性能之间进行权衡。实验结果表明,与基于chord的系统相比,我们的技术将延迟提高了74%,并使我们能够平衡存储使用量和远程数据传输。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimizing WAN Communications in Inter-datacenter Key-Value Stores
Cloud-federations have emerged as popular platforms for Internet-scale services. Cloud-federations are running over multiple datacenters, because a cloud-federation is an aggregate of cloud services each of which runs in a single datacenter. In such inter-datacenter environments, distributed key-value stores (DKVSs) are attractive databases in terms of scalability. However, inter-datacenter communications degrade the performance of these DKVSs because of their large latency and narrow bandwidth. In this paper, we demonstrate how to reduce and hide the weak points of inter-datacenter communications for DKVSs. To solve the problems we introduce two techniques called multi-layered DHT (ML-DHT) and local-first data rebuilding (LDR). ML-DHT provides a global and consistent index of key-value pairs with the efficient expandability of the storage capacity. It employs a routing protocol which reduces routing hops that pass through interdatacenter connections. LDR reduces data transfer on interdatacenter connections by using erasure coding techniques. It enables KVS administrators to flexibly make trade-offs between expandability of storage capacity and the performance of data transfer. Experimental results demonstrate that our techniques improve the latency up to 74 % compared with a Chord-based system and enable us to balance the amount of storage usage and remote data transfer.
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