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Profile-driven cache management

机译：配置文件驱动的缓存管理

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Modern distributed information systems cope with disconnection and limited bandwidth by using caches. In communication-constrained situations, traditional demand-driven approaches are inadequate. Instead, caches must be preloaded in order to mitigate the absence of connectivity or the paucity of bandwidth. We propose to use application-level knowledge expressed as profiles to manage the contents of caches. We propose a simple, but rich profile language that permits high-level expression of a user's data needs for the purpose of expressing desirable contents of a cache. We consider techniques for prefetching a cache on the basis of profiles expressed in our framework, both for basic and preemptive prefetching, the latter referring to the case where staging a cache can be interrupted at any point without prior warning. We examine the effectiveness of three profile processing techniques, and show that the rich expressivity of our profile language does not prevent a fairly simple greedy algorithm from being an effective processing technique. We also show that for a large shared cache, multiple clients' profiles can be combined into a single superprofile that is representative of them all, but that when the number of clients with profiles is significantly large, a randomized approach is more scalable than a greedy approach. We believe that profiles, as described, are an enabling technology that could spawn a rich new area of research beyond cache management into network data management in general.
机译：现代分布式信息系统通过使用缓存来应对断开连接和有限的带宽。在通信受限的情况下，传统的需求驱动方法是不够的。取而代之的是，必须预先加载缓存，以减轻缺少连接性或带宽不足的情况。我们建议使用表示为配置文件的应用程序级知识来管理缓存的内容。我们提出了一种简单但丰富的配置文件语言，该语言允许高层表达用户的数据需求，以表达所需的缓存内容。我们考虑基于框架中表示的配置文件预取缓存的技术，包括基本预取和抢先式预取，后者指的是可以在没有事先警告的情况下随时中断暂存的情况。我们检查了三种配置文件处理技术的有效性，并证明了我们配置文件语言的丰富表达能力并没有阻止相当简单的贪婪算法成为一种有效的处理技术。我们还表明，对于大型共享缓存，可以将多个客户端的配置文件组合为一个代表所有客户端的超级配置文件，但是当具有配置文件的客户端数量非常大时，随机方法比贪婪方法更具可扩展性方法。我们认为，如上所述，配置文件是一种启用技术，它可能会产生从高速缓存管理到网络数据管理的广泛的新研究领域。

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