Distributed cloud computing environments rely on sophisticated communication and sharing paradigms for ease of access, information processing, and analysis. The challenging characteristic of such cloud computing environments is the concurrency and access as both the service provider and end-user rely on the common sharing platform. In this article, retrieval and storage-based indexing framework (RSIF) is designed to improve the concurrency of user and service provider access to the cloud-stored healthcare data. Concurrency is achieved through replication-free and continuous indexing and time-constrained retrieval of stored information. The process of classifying the constraints for data augmentation and update is performed using deep learning for all the storage instances. Through conditional assessment, the learning process determines the approximation of indexing and ordering for storing and retrieval, respectively. This helps to reduce the time for access and retrieval concurrently, provided the process is not dependent. The simulation analysis using the metrics discontinuous indexing, replicated data, retrieval time, and cost proves the reliability of the proposed framework.

中文翻译：

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分布式云计算环境中使用深度学习的并发医疗数据处理和存储框架

分布式云计算环境依靠复杂的通信和共享范例来简化访问，信息处理和分析。这种云计算环境的挑战性特征是并发性和访问性，因为服务提供商和最终用户都依赖于公共共享平台。在本文中，基于检索和存储的索引框架（RSIF）旨在提高用户和服务提供商对云存储的医疗保健数据的访问并发性。并发是通过无复制和连续索引以及受时间限制的存储信息检索来实现的。使用深度学习对所有存储实例执行对数据扩充和更新的约束进行分类的过程。通过条件评估 学习过程确定分别用于存储和检索的索引和排序的近似值。如果该过程不受依赖，则这有助于减少同时访问和检索的时间。使用度量标准不连续索引，复制数据，检索时间和成本进行的仿真分析证明了所提出框架的可靠性。