The use of key-value caches in modern web servers is becoming more and more ubiquitous. Representatively, Memcached as a widely used key-value cache system, originally intended for speeding up dynamic web applications by alleviating database load. One of the key factors affecting the performance of Memcached is the memory allocation among different item classes. How to obtain the most efficient partitioning scheme with low time and space consumption is a focus of attention. In this paper, we propose a lightweight and accurate memory allocation scheme in Memcached, by sampling access patterns, analyzing data locality, and reassigning the memory space. One early study on optimizing memory allocation is LAMA, which uses footprint-based MRC to optimize memory allocation in Memcached. However, LAMA does not model deletion operations in Memcached and its spatial overhead is quite large. We propose a method that consumes only 3% of LAMA space and can handle read, write and deletion operations. Moreover, evaluation results show that the average stable-state miss ratio is reduced by 15.0% and the average stable-state response time is reduced by 12.3% when comparing our method to LAMA.

中文翻译：

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Key-Value Cache中轻量且准确的内存分配

在现代 Web 服务器中使用键值缓存变得越来越普遍。代表性地，Memcached 作为一种广泛使用的键值缓存系统，最初旨在通过减轻数据库负载来加速动态 Web 应用程序。影响 Memcached 性能的关键因素之一是不同 item 类之间的内存分配。如何以低的时间和空间消耗获得最高效的分区方案是人们关注的焦点。在本文中，我们通过采样访问模式、分析数据局部性和重新分配内存空间，在 Memcached 中提出了一种轻量级且准确的内存分配方案。一项关于优化内存分配的早期研究是 LAMA，它使用基于内存占用的 MRC 来优化 Memcached 中的内存分配。然而，LAMA 在 Memcached 中没有对删除操作进行建模，其空间开销非常大。我们提出了一种仅消耗 3% LAMA 空间并且可以处理读、写和删除操作的方法。此外，评估结果表明，与 LAMA 方法相比，平均稳态未命中率降低了 15.0%，平均稳态响应时间降低了 12.3%。