Customizable Scale-Out Key-Value Stores
Customizable Scale-Out Key-Value Stores
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DOI：
10.1109/tpds.2020.2982640
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发表时间：
2020-03
影响因子：
5.3
通讯作者：
Ali Anwar;Yue Cheng;Hai Huang;Jingoo Han;Hyogi Sim;Dongyoon Lee;F. Douglis;A. Butt
中科院分区：
文献类型：
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作者：
Ali Anwar;Yue Cheng;Hai Huang;Jingoo Han;Hyogi Sim;Dongyoon Lee;F. Douglis;A. Butt
企业级KV商店通常不太适合HPC应用程序，因此需要进行繁琐的端到端KV设计定制以满足现代HPC应用程序的需求。为此，在本文中，我们提出了BespoKV，这是一个自适应、可扩展和横向扩展的KV存储框架。BestpoKV将KV门店设计解耦为用于分布式管理的控制平面和用于本地数据存储的数据平面。对于控制平面，BespoKV提供预置模块，称为Controllet，支持常见的分布式功能(例如，复制、一致性和拓扑)及其各种组合。这种解耦允许BestpoKV采用用户提供的单服务器KV商店，称为DataLet，并透明地启用可扩展和容错的分布式KV商店服务。由此产生的分布式存储还可以适应一致性或拓扑要求的变化，并且可以很容易地为新的服务类型进行扩展。此类专业化认证支持在HPC应用程序中创新地使用KV商店，特别是对于使用KV友好工作负载的新兴应用程序。我们在本地试验台和公共云环境中对BespoKV进行了评估。实验表明，支持定制KV的分布式KV存储横向扩展到大量节点，性能相当，有时比最先进的系统高1.2到2.6倍。
Enterprise KV stores are often not well suited for HPC applications, and thus cumbersome end-to-end KV design customization is required to meet the needs of modern HPC applications. To this end, in this article we present bespoKV, an adaptive, extensible, and scale-out KV store framework. bespoKV decouples the KV store design into the control plane for distributed management and the data plane for local data store. For the control plane, bespoKVprovides pre-built modules, called controlets, supporting common distributed functionalities (e.g., replication, consistency, and topology) and their various combinations. This decoupling allows bespoKV to take a user-provided single-server KV store, called a datalet, and transparently enables a scalable and fault-tolerant distributed KV store service. The resulting distributed stores are also adaptive to consistency or topology requirement changes and can be easily extended for new types of services. Such specializations enable innovative uses of KV stores in HPC applications, especially for emerging applications that utilize KV-friendly workloads. We evaluate bespoKV in a local testbed as well as in a public cloud settings. Experiments show that bespoKV-enabled distributed KV stores scale horizontally to a large number of nodes, and performs comparably and sometimes 1.2× to 2.6× better than the state-of-the-art systems.