Optimizing Data Flow between Edge Systems and Cloud Platforms for Performance-Critical Applications

Abstract

Slow data transfer between edge devices and cloud platforms has become a serious speed issue as the volume of data and application requirements increase. Edge-cloud systems struggle with applications that require a large amount of data and dislike waiting. Some of these issues include insufficient bandwidth, an unreliable network, edge locations with different computer capabilities, and the inability to observe how all of the data flows. If you don't properly arrange your data flow, you risk providing too much information, taking too long to make decisions, and spending too much money running your firm. It is difficult to create data flow systems that perform well and survive a long time since there are so many different types of edge hardware, links that fail, and data formats that might be employed. This paper discusses the need of using the optimal data flow designs that mix edge processing and coordination with cloud analytics. It demonstrates how to improve things by leveraging smart job assignment, understanding where data is, and allowing data to flow in various ways across the edge-cloud continuum. The strategy focuses on edge-based data filtering, grouping, and compression to minimize unnecessary data transfers. Cloud platforms are also utilized for data storage, advanced analytics, and long-term learning. When determining the best answer, tasks that must operate swiftly and reliably with little delay are taken into account. The major findings demonstrate that end-to-end lag has decreased, bandwidth consumption has decreased, and the system is now better prepared to withstand network changes. This article will help system architects and engineers working on fast next-generation distributed systems learn about an architectural framework and optimization approaches that make it easier for data to travel between edge and cloud platforms.