A Dynamic Data Middleware Cache for Rapidly-growing Scientific Repositories

TL;DR

Delta is a dynamic middleware cache system designed for rapidly-growing scientific repositories, intelligently decoupling data objects to minimize network costs and improve scalability in the face of frequent updates and queries.

Contribution

It introduces a novel decision framework that adaptively manages data caching based on workload profiling, leveraging network flow concepts for optimal data decoupling.

Findings

Reduces network costs in scientific data access.

Improves cache efficiency for dynamic, high-growth repositories.

Demonstrates effectiveness through real astronomy survey data.

Abstract

Modern scientific repositories are growing rapidly in size. Scientists are increasingly interested in viewing the latest data as part of query results. Current scientific middleware cache systems, however, assume repositories are static. Thus, they cannot answer scientific queries with the latest data. The queries, instead, are routed to the repository until data at the cache is refreshed. In data-intensive scientific disciplines, such as astronomy, indiscriminate query routing or data refreshing often results in runaway network costs. This severely affects the performance and scalability of the repositories and makes poor use of the cache system. We present Delta, a dynamic data middleware cache system for rapidly-growing scientific repositories. Delta's key component is a decision framework that adaptively decouples data objects---choosing to keep some data object at the cache, when they are heavily queried, and keeping some data objects at the repository, when they are heavily updated. Our algorithm profiles incoming workload to search for optimal data decoupling that reduces network costs. It leverages formal concepts from the network flow problem, and is robust to evolving scientific workloads. We evaluate the efficacy of Delta, through a prototype implementation, by running query traces collected from a real astronomy survey.