# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 1 Introduction
FQ [19; 20; 21; 22; 23; 24] allocates an equal bandwidth to each user based on their current bandwidth usage, while FCFS [25; 26; 27] generates a service stack based on a user's time series and follows the principle of first come, first served. These two offloading models are well-established and commonly used in modern cloud networks, forming the foundation of the Internet. While FQ can offer services to all users simultaneously, it has limitations. As the bandwidth is fixed, increasing the number of users can result in congestion, which means that not all users can request services [28; 29]. Additionally, FCFS has its limitations, such as a small number of users and long response times for users [30]. In this paper, we propose a data offloading strategy that can be implemented in both traditional cloud computing architecture and cloud-edge collaboration architecture. This strategy involves grouping users who have not requested services with nodes that have requested services, resulting in multiple link groups for data offloading. This approach allows for increased user requests that can be handled simultaneously. Once the service request of a preceding node is completed, it collaborates with subsequent nodes to offload the data, thereby reducing delays. WFO enables multiple users to access the same data in a data center simultaneously, with three main features. Firstly, edge nodes are queued based on their time series and allocated bandwidth according to their actual requirements until no more bandwidth is available for the cloud. Nodes that come after are then mounted onto the pre-existing nodes within the user's tolerance delay to create multiple packet links. Second, the priority of the edge nodes is calculated within the group, and data offloading is sorted accordingly. Once the priority level is determined, data offloading and segmentation are based on this arrangement. The data then flows through the arranged nodes to the next node. When the first node stops receiving data, the cloud offloads the data to subsequent nodes. Third, data offloading and segmentation are performed as per the arrangement. The data flows through the arranged nodes to the next node. Once the first node stops receiving data, the cloud offloads the data to the subsequent nodes. We conducted this study due to the need for data offloading in cloud-edge coordination networks and customer requirements for high-quality services. **The motivation can be summarized as follows:**Use the limited bandwidth resources of the cloud and edge to provide services for more users when the number of users increases significantly. When offloading time-sensitive data, ensure data validity and reduce offloading delay. Reduce the usage of cloud and edge infrastructure resources using a data offloading strategy for cloud and edge collaboration. * Based on historical experience, when many users suddenly access a data center, the bandwidth faces tremendous pressure. Users often have to wait for services. For example, in live broadcasts of large-scale sports events, the user terminal often appears to stall and pause. This is because physical resources limit the number of services available to the users. This can be improved by adding physical infrastructure. However, there are significant costs involved. We hope to present innovations for algorithms and system architectures to maximize system performance under a limited bandwidth. * With an increase in video traffic in the network, the timeliness of data must be considered. The timeliness of data is related to the user's service experience. Therefore, delay in data offloading was a crucial factor in this study. * Under the development trend of cloud-side collaboration, the traditional data offloading method cannot provide its advantages in such an architecture. Data offloading is the foundation of cloud-edge collaboration. However, there is still little research on data offloading, in which data flows are sent from the data center to the edge nodes.