# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 1 Introduction  
Data offloading of cloud-edge collaboration has been the focus of cloud- and edge-computing in recent years. As video data on the internet increases, traditional cloud computing needs improvement to ensure user service. However, the emergence of edge computing compensates for the deficiencies of cloud computing. The data offloading strategy of cloud-edge collaboration can serve as many users as possible while ensuring service quality, especially in situations with limited network bandwidth. The cloud-edge collaboration will be the central technology in upcoming networks [1]. The data offloading strategy of cloud-edge collaboration is a vital component of this advancement. Cloud computing has emerged as a prominent industry in information technology due to its ability to offer a diverse range of services. It is considered a vital resource pool with elastic and flexible applications, dynamic extensibility, and a high availability of performance and hosting services. Over the past decade, the academic and industrial fields have extensively researched cloud computing. According to reports by IDC and CNNIC, internet access terminals have exceeded 50 billion, with video users accounting for 94.5% [2]. As the number of network users continues to grow, more than cloud computing's data offloading capacity is required for services that require real-time performance. Furthermore, uploading and offloading video streams strain the cloud data center's network bandwidth. Edge computing was developed to address the issues of data transmission delays and bandwidth consumption in cloud computing. In cloud and IoT services, downstream data refers to cloud services, while upstream data refers to IoT services [3]. By leveraging the proximity of users to edge computing nodes, services can be obtained from the edge, leading to a significant reduction in data processing delays and bandwidth consumption associated with cloud computing. With the growing research on edge computing, it hasbecome apparent that cloud-edge collaboration will emerge as a new computing mode and a promising area for further investigation [1; 4]. Cloud-edge collaboration [5] has widened in many directions, such as computation offloading [6; 7], data offloading [8; 9], data sharing [10; 11], resource allocation [12; 13], and privacy protection [14]. Data collaboration is a crucial research area in cloud-edge collaboration. Data offloading is indispensable for data collaboration [3]. In cloud-edge collaboration architecture, the user prioritizes service quality over the source of the service, whether it is from the cloud or edge computing. Edge computing addresses the issue of cloud computing by bringing the service closer to the user, thereby reducing the distance between users and the service. Integrating cloud and edge computing in a collaborative computing mode can alleviate the operational pressure of cloud data centers and enhance the reliability of network services. Cloud data centers must swiftly transfer data to edge nodes to disseminate video data throughout the network. Hence, exploring data offloading in cloud-edge collaboration can leverage the benefits of both cloud and edge computing to deliver superior services to users while reducing the congestion probability of the central node, increasing the data uninstallation rate, and reducing user delays [2]. According to a 2018 report on _Global Internet Phenomena_, video data makes up nearly 58% of downstream network traffic [15]. Due to its large size, video flow requires more bandwidth and transmission time than other data types, which significantly strains cloud data centers, often leading to congestion issues [16]. The conventional cloud-computing service model provides users with fair queue (FQ) [17; 18] and first-come-first-served (FCFS) strategies. These strategies were initially designed for scheduling purposes but have evolved into a reliable data-offloading strategy in our current application scenario.