Linearizable Low-latency Reads at the Edge

Joshua Guarnieri, Aleksey Charapko
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Abstract

Edge computing enables moving data closer to users to reduce latency and improve user experience. Edge data centers are capable and reliable enough to support various data management solutions, such as caches and data stores. Unfortunately, edge storage systems sacrifice consistency to benefit from geographical proximity to users. In this paper, we present EdgePQR, a strongly consistent, edge-aware data store that allows edge clients to read "hot" data locally at the edge with low latency. EdgePQR relies on the piece-wise defined quorums consisting of nodes in a core cloud system and one or more edge data centers to replicate data to the edge. It then uses an edge quorum to query data locally. EdgePQR ensures safety by enforcing intersections between all vital quorums: any leader election and replication quorums intersect, and any replication and edge-read quorums intersect as well.
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在边缘处线性化低延迟读取
边缘计算使数据更接近用户,从而减少延迟并改善用户体验。边缘数据中心的能力和可靠性足以支持各种数据管理解决方案,如缓存和数据存储。不幸的是,边缘存储系统牺牲了一致性,从而受益于与用户的地理位置接近。在本文中,我们提出了EdgePQR,这是一种强一致性、边缘感知的数据存储,允许边缘客户端以低延迟在边缘本地读取“热”数据。EdgePQR依赖于由核心云系统中的节点和一个或多个边缘数据中心组成的分段定义仲裁来将数据复制到边缘。然后,它使用边缘仲裁在本地查询数据。EdgePQR通过强制所有重要群体之间的交叉来确保安全性:任何leader选举和复制群体都会相交,任何复制群体和边缘读取群体也会相交。
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