Coupling Prefix Caching and Collective Downloads for Remote Data Access

Abstract

Scientific datasets are typically archived at mass storage systems or data centers close to supercomputers/instruments. Endusers of these datasets, however, usually perform parts of their workflows at their local computers. In such cases, client-side caching can offer significant gains by reducing the cost of widearea data movement. Scientific data caches, however, traditionally cache entire datasets, which may not be necessary. In this paper, we propose a novel combination of prefix caching and collective download. Prefix caching allows the bootstrapping of dataset downloads by caching only a prefix of the dataset, while collective download facilitates efficient parallel patching of the missing suffix from an external data source. To estimate the optimal prefix size, we further present an analytical model that considers both the initial download overhead and the downloading speed. We implemented our proposed approach in the FreeLoader distributed cache prototype. Experimental results (using multiple scientific data repositories and data transfer tools, as well as a real-world scientific dataset access trace) demonstrate that prefix caching and collective download can be implemented efficiently, our model can select an appropriate prefix size, and the cache hit rate can be improved significantly without hurting the local access rate of cached datasets.