Solid State Drives (SSDs) are popularly used for caching in large scale cloud storage systems nowadays. Traditionally, most cache algorithms make replacement upon each miss when cache space is full. However, we observe that in a typical Cloud Block Storage (CBS) system, there is a great percentage of blocks with large reuse distances, which would result in large number of blocks being evicted out of the cache before they ever have a chance to be referenced while they are cached, significantly jeopardizing the cache efficiency. In this article, we propose LEA, Lazy Eviction cache Algorithm, for cloud block storage to efficiently remedy the cache inefficiencies caused by cache blocks with large reuse distances. LEA mainly employs two lists, Lazy Eviction List (LEL) and Block Identity List (BIL), which keep track of two types of victim blocks respectively based on their cache duration when replacements occur, to improve cache efficiency. When a cache miss happens, if the victim block has not resided in cache for longer than its reuse distance, LEA inserts the missed block identity into BIL. Otherwise, it inserts the missed block entry into LEL. We have evaluated LEA by using IO traces collected from Tencent, one of the largest network service providers in the world, and several open source traces. Experimental results show that LEA not only outperforms most of the state-of-the-art cache algorithms in hit ratio, but also greatly reduces the number of SSD writes.

中文翻译：

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通过利用块重用距离实现云块存储的高效 SSD 缓存


如今，固态硬盘（SSD）广泛用于大规模云存储系统中的缓存。传统上，当缓存空间已满时，大多数缓存算法会在每次未命中时进行替换。然而，我们观察到，在典型的云块存储（CBS）系统中，有很大比例的块具有较大的重用距离，这将导致大量块在有机会被删除之前就被从缓存中逐出。在缓存时引用它们，这会严重损害缓存效率。在本文中，我们针对云块存储提出了 LEA（Lazy Eviction 缓存算法），以有效地解决因重用距离较大的缓存块而导致的缓存低效问题。 LEA主要采用惰性逐出列表（LEL）和块标识列表（BIL）两种列表，它们在发生替换时根据缓存持续时间分别跟踪两种类型的受害者块，以提高缓存效率。当发生缓存未命中时，如果受害块在缓存中驻留的时间没有超过其重用距离，LEA 会将丢失的块标识插入到 BIL 中。否则，它将丢失的块条目插入到 LEL 中。我们使用从全球最大的网络服务提供商之一腾讯收集的 IO 跟踪以及多个开源跟踪来评估 LEA。实验结果表明，LEA不仅在命中率上优于大多数最先进的缓存算法，而且还大大减少了SSD的写入次数。