An Intelligent Cloud Storage Gateway for Medical Imaging

TL;DR

This paper introduces an intelligent cloud storage gateway with a novel cache architecture for medical imaging, significantly reducing retrieval times and latency while adapting to different institutional workflows.

Contribution

It presents a new cache system combining static rules and pattern recognition, optimizing data access in cloud-based medical imaging repositories.

Findings

Cache hit ratios reach around 80%

Image retrieval times are reduced by over 60%

Latency is significantly decreased with small cache sizes

Abstract

Historically, medical imaging repositories have been supported by indoor infrastructures. However, the amount of diagnostic imaging procedures has continuously increased over the last decades, imposing several challenges associated with the storage volume, data redundancy and availability. Cloud platforms are focused on delivering hardware and software services over the Internet, becoming an appealing solution for repository outsourcing. Although this option may bring financial and technological benefits, it also presents new challenges. In medical imaging scenarios, communication latency is a critical issue that still hinders the adoption of this paradigm. This paper proposes an intelligent Cloud storage gateway that optimizes data access times. This is achieved through a new cache architecture that combines static rules and pattern recognition for eviction and prefetching. The evaluation results, obtained through simulations over a real-world dataset, show that cache hit ratios can reach around 80%, leading reductions of image retrieval times by over 60%. The combined use of eviction and prefetching policies pro- posed can significantly reduce communication latency, even when using a small cache in comparison to the total size of the repository. Apart from the performance gains, the proposed system is capable of adjusting to specific workflows of different institutions.