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Data prefetching aims to improve access times to data storage systems bypredicting data records that are likely to be accessed by subsequent requestsand retrieving them into a memory cache before they are needed. In the case ofPersistent Object Stores, previous approaches to prefetching have been based onpredictions made through analysis of the store's schema, which generates rigidpredictions, or monitoring access patterns to the store while applications areexecuted, which introduces memory and/or computation overhead. In this paper,we present CAPre, a novel prefetching system for Persistent Object Stores basedon static code analysis of object-oriented applications. CAPre generates thepredictions at compile-time and does not introduce any overhead to theapplication execution. Moreover, CAPre is able to predict large amounts ofobjects that will be accessed in the near future, thus enabling the objectstore to perform parallel prefetching if the objects are distributed, in a muchmore aggressive way than in schema-based prediction algorithms. We integrateCAPre into a distributed Persistent Object Store and run a series ofexperiments that show that it can reduce the execution time of applicationsfrom 9% to over 50%, depending on the nature of the application and itspersistent data model.

CAPre: Code-Analysis based Prefetching for Persistent Object Stores