Profile-driven cache management

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.