# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 1 Introduction  
In many scientific disciplines, a huge amount of data is being generated. In the X-ray Free Electron Laser device (XFEL), the average data rate has reached more than 2 GB/s, the peak rate has reached 100 GB/s, and the data storage capacity has exceeded 100 PB [1]. Significant challenges exist in the collection and storage of data by large scientific facilities. The ever-increasing data generation speed and the sheer quantity of data require effective tools to collect and filter the data, save the data through high-speed networks, and prevent the loss of valuable data. Due to the storage capability limitations of existing computer systems, it is too late to completely record and save rapidly generated data [2]. However, there may be an opportunity for more nodes to participate in storage via high-speed networks. In addition, a large amount of data not only needs to be stored properly, but also needs to be able to be efficiently accessed. Due to the increase in the quantity of data, it is becoming increasingly difficult to read and move data. To deal with these challenges, a common approach is to build a data center on site, connect the experimental station with an optical fiber network/InfiniBand, and equip the data center with high-speed storage and computing equipment (CPU, GPU) for scientific computing. However, a bottleneck remains in network throughput and storage capacity in these solutions, because they still use end-to-end transmission, and provide limited improvements using the shortest routing path or multiple paths in the transport layer. Furthermore, distributing the large quantity of raw data and making this data available to scientists around the world in a scalable manner are additional typical challenges. Recently, Named Data Networking (NDN) [3; 4] and big science communities have combined cache and forwarding strategies, and used the cache capability to accelerate the distribution and management for big science experimental data [5; 6; 7; 8; 9]. In [5], the author proposes the federating of distributed catalog systems that store and manage NDN names to increase the speed of discovery of desired data. The work of [6] presented a preliminary study addressing opportunities and challenges to enable NDN-based intelligent data retrieval in networks for high-energy physics (HEP). However, these studies focused on the management of a huge quantity of scientific data and data retrieval, and rarely involved the persistent storage of collected data in networks. As a result of the development of networking and storage, the forwarding rate of the interface commonly exceeds the storage bandwidth in a single network node. However, other switches in the network can have high speeds and large persistent storage capabilities. To better handle high-speed data injection, multiple switches can cooperate to store heavy data storage traffic. Using the network as a means of storage, the data can be stored along the path, which facilitates edge processing and avoids sending data to the cloud for reading, processing, and analysis, thus saving network bandwidth and increasing the responsiveness of the applications. However, no research has been conducted on directly using the network as a form of permanent storage, which would allow producers to simply offload their data to the network and let the network manage the storage and access to data. This paper proposes the realization of data storage in the forwarding process, and to cooperatively offload the data beyond the local storage capacity to neighbor storage nodes. The main contributions of our work are as follows:  
1. To address high-speed data storage, we proposed an in-network storage service node structure to support cooperative storage in neighbor nodes, and designed an identifier (ID)-based cooperative storage protocol. 2.