重新审视有界负载的一致性哈希
Revisiting Consistent Hashing with Bounded Loads
摘要：
动态负载均衡是分布式缓存的核心。这里，目标是以提供负载均衡的方式将对象（负载）分配给服务器（计算节点），同时动态地调整服务器的添加或移除。一个基本要求是，添加或删除小型服务器不应该要求我们重新计算整个分配。一个流行且被广泛采用的解决方案是二十年历史的一致性哈希（CH）。最近，提供了一个优雅的扩展来说明服务器边界。在本文中，我们确定，现有的方法CH及其变种遭受级联溢出，导致不良的负载均衡。这种级联效应导致哈希过程的性能随着负载的增加而降低。为了克服级联效应，我们提出了一个简单的解决方案，CH的基础上快速minwise哈希的最新进展。我们表明，理论和经验，我们提出的解决方案是显着优于负载均衡，在许多意义上是最佳的。在AOL搜索数据集和印第安纳大学点击数据集与真实的用户活动，我们提出的解决方案减少了几个数量级的缓存未命中。
英文摘要：
Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. One essential requirement is that the addition or removal of small servers should not require us to recompute the complete assignment. A popular and widely adopted solution is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.
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