Optimizing Accounting Informatization through Simultaneous Multi-Tasking across Edge and Cloud Devices using Hybrid Machine Learning Models

Abstract

Accounting informatization is a crucial component of enterprise informatization, significantly impacting operational efficiency in accounting and finance. Advances in information technology have introduced automation techniques that accelerate the processing of accounting information cost-effectively. Integrating artificial intelligence, cloud computing, and edge computing is pivotal in streamlining and optimizing these processes. Traditionally, accounting informatization relied on system servers and local storage for data processing. However, the era of big data necessitates a shift to cloud computing frameworks for efficient data storage and processing. Despite the advantages of cloud storage, concerns arise regarding data security and the substantial data transactions between the cloud and source devices. To address these challenges, this research proposes a novel algorithm, Heterogeneous Distributed Deep Learning with Data Offloading (DDLO) algorithm. DDLO leverages the synergy between edge devices and cloud computing to enhance data processes. Edge computing enables rapid processing of large volumes of data at or near the data collection sites, optimizing day-to-day operations for enterprises. Furthermore, machine learning algorithms at edge devices enhance data processing efficiency, augmenting the computing environment's overall performance. The proposed DDLO algorithm fosters a hybrid machine learning approach for computing joint tasks and multi-tasking in accounting informatization. It enables dynamic resource allocation, allowing selected data or model updates to be offloaded to the cloud for complex tasks. The algorithm's performance is rigorously evaluated using key metrics, including computing time, offloading time, accuracy, and cost levels. By capitalizing on the strengths of edge computing, cloud computing, and artificial intelligence, the DDLO algorithm effectively addresses accounting informatization challenges. It empowers enterprises to process vast amounts of accounting data efficiently and securely while improving overall operational efficiency. Regarding time, using terasort in tasks offloading using DDLO consumes less milliseconds 0t 33 ms which is lesser than other techniques.