Fetching Files with An Integrated Deep Learning Model and Optimisation Assistance in A Distributed File System

Abstract

Distributed storage and computing techniques have replaced centralized computing for newlineexecuting applications which have to process large data. Distributed systems (DS) offer the newlineability to store vast amounts of scalable data and expedite computations. Distributed File newlineSystem (DFS) is used as the back-end storage system by cloud computing systems, where most newlineBig Data applications are installed. Prefetching and client-side caching are two key methods newlinefor enhancing DFS performance. The most common file operations in a distributed file system newlineenvironment are read operations, whereas write operations are executed on files less frequently. newlineIn the landscape of modern computing, distributed storage and computing techniques have newlineemerged as the cornerstone for executing applications dealing with large-scale data processing newlinetasks. These distributed systems (DS) offer unparalleled scalability and efficiency compared to newlinetraditional centralized computing paradigms. At the heart of many distributed computing newlineinfrastructures lies the Distributed File System (DFS), serving as the backbone storage system newlinefor cloud computing platforms where a plethora of Big Data applications reside newline