# Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks
## 2 Related Work  
This section, introduces the topology of a DCN and then summarizes the advantages and disadvantages of load balancing in a DCN. Finally, the RRS and WRRS algorithms are presented. The rapid growth and scalability of data is a challenging task in DCNs. Research on DCN topology can be divided into two categories: server-centered and network device-centered topologies. In the server-centered topology, the network topology is constructed in a recursive way. The server is not only a computing unit, but also a routing node, so it will actively participate in packet forwarding. In the network device-centered topology, network traffic routing and forwarding are all completed by switches or routers [25]. Server-centered topologies include the DCell, FiCoon, Bcube topologies [26]. The DCell topology recursively constructs a large-scale network through a low-end port switch and multi-port server. The limitations of DCell include a cross-sectional bandwidth and network latency [27]. By contrast, network device-centered topology include the Tree, Fat-Tree, and VL2 topologies [28]. In the Fat-Tree structure, the topology of the network is divided into three levels, namely, the core, the aggregation, and the access layers. The Fat-Tree bisection bandwidth increases with the expansion of the network size, so it can provide high throughput transmission service for data center. Multiple parallel paths occur between the source and destination nodes in the communication between the servers of different pods, such that the network has acceptable fault tolerance performance and generally no single point of failure arises. Replacing high performance switching equipment greatly reduces network equipment overhead. Moreover, the network diameter is small, a feature which can ensure the real-time network requirements of video, online meeting, and other services. The topology structure is also regular and symmetrical, thereby making it conducive to network cabling, automatic configuration, optimization, and upgrading [29]. The software defined network (SDN) paradigm is usually adopted by DCNs to provide centralized control and network program ability by separating network control and data forwarding functions [30]. Thus, SDN can simplify network management and greatly promote the improvement of network performance and network innovation ability so as to solve the load balancing problem of DCNs [31]. The separation of network control and data forwarding is the key element to the success of SDN technology [32]. However, the centralized control mode in logic also generates the challenge of scalability [33]. Although the OpenFlow protocol in SDN has attracted great attention [34], many technical problems remain unresolved in terms of the OpenFlow protocol itself or the SDN control separation architecture [35]. The main problems include the scalability of the control plane [36], the design of the SDN forwarding plane, and the consistency of the SDN control logic. The main goal of improving the scalability of the control plane is to reduce the overhead between the data plane and controller, that is, to reduce network congestion, improve network transmission efficiency, reduce network delay, and ensure network load balance so as to reduce the burden of the controller, improve the efficiency of the data plane and enhance network performance. In order to reduce the training time and improve the training efficiency in the comparative test, we chose the polling algorithm with a faster training speed than the recurrent neural network (RNN) this kind of algorithm [37]. The following is an overview of the RRS and WRRS algorithms. The RRS algorithm is implemented by forwarding requests to different servers in turn. The advantages of this algorithm are simplicity, stateless scheduling, and the omission of the need to record the current connection state. The RRS algorithm is implemented on the premise that all servers have the same processing performance and the current response speed and the number of connections of each server are irrelevant.