SessionStore:用于边缘的会话感知数据存储

S. H. Mortazavi, Mohammad Salehe, Bharath Balasubramanian, E. D. Lara, S. Narayanan
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引用次数: 9

摘要

对于设计在边缘数据中心上运行的存储系统来说,通过依赖最终一致的模型来复制数据来避免与地理分布相关的高延迟是很常见的。最终一致性对于许多边缘应用程序来说工作得很好,因为只要客户端与相同的副本交互,存储系统就可以提供会话一致性,这是一种更强的一致性模型,具有两个额外的重要属性:(i) read-you -writes,其中客户端更新对象的后续读取将返回更新的值或更新的值;(ii)单调读取,如果客户端已经看到了一个对象的特定值,后续读取将返回相同的值或更新的值。虽然会话一致性不能保证不同的客户端将以相同的顺序感知更新,但它仍然为每个单独的客户端提供了与客户端自己的行为一致的直观视图。不幸的是,随着时间的推移,当客户机与位于不同数据中心的多个副本交互时,这些一致性保证就会失效,原因可能是应用程序分区,也可能是客户机或代码迁移。SessionStore是用于雾/边缘计算的数据存储,它确保会话一致性在其他最终一致的副本之上。SessionStore通过将相关数据访问分组到一个会话中来增强会话一致性,并使用会话感知协调算法在副本之间切换时仅协调与会话相关的数据。与完全副本协调相比,这种方法最多可减少90%的数据传输和延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SessionStore: A Session-Aware Datastore for the Edge
It is common for storage systems designed to run on edge datacenters to avoid the high latencies associated with geo-distribution by relying on eventually consistent models to replicate data. Eventual consistency works well for many edge applications because as long as the client interacts with the same replica, the storage system can provide session consistency, a stronger consistency model that has two additional important properties: (i) read-your-writes, where subsequent reads by a client that has updated an object will return the updated value or a newer one; and, (ii) monotonic reads, where if a client has seen a particular value for an object, subsequent reads will return the same value or a newer one. While session consistency does not guarantee that different clients will perceive updates in the same order, it nevertheless presents each individual client with an intuitive view of the world that is consistent with the client’s own actions. Unfortunately, these consistency guarantees break down when a client interacts with multiple replicas housed on different datacenters over time, either as a result of application partitioning, or client or code mobility.SessionStore is a datastore for fog/edge computing that ensures session consistency on a top of otherwise eventually consistent replicas. SessionStore enforces session consistency by grouping related data accesses into a session, and using a session-aware reconciliation algorithm to reconcile only the data that is relevant to the session when switching between replicas. This approach reduces data transfer and latency by up to 90% compared to full replica reconciliation.
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