# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 1 Introduction  
The growing consumption of electricity attracts more attention to power quality, environmental protection, and cost. Instead, the traditional power grid model [1] has low efficiency and many unsafe factors, making it difficult to meet the increasing demand for electricity. Thus smart grid [2] (SG) is proposed to improve the situation. SGs [2], as electricity supply networks, bring new solutions to optimize the operations of all functional units from electricity generation to end-customers through communication technologies. By introducing the communication system layer to control, monitor, and automate the entire network of power systems [3, 4], the SG becomes more flexible. The hierarchy of the SG is shown in Figure 1. Nowadays, with the implementation of many recent network technologies [5, 6, 7, 8, 9], the power grid has new requirements on availability and reliability. As shown in Figure 1, there are many applications in the power system layer (a), including power production units, power transmission networks, power distribution networks, etc. Communication serves as the backbone in the creation of all the involved applications and technologies. According to characteristics of diverse applications, the smart grid applies different communication technologies in the communication layer (b) to make the power system intelligent. Specifically, the SG supports different applications based on the integrated high-speed two-way communication network, such as advanced measurement system, demand response (DR) [10], and distribution automation (DA) [11]. Moreover, the data communication network associated with the power grid consists of three parts: the wide area network (WAN), the neighborhood area network (NAN), and the home area network (HAN). As the \"last kilometer\" of data collection and user access in theSG, NAN needs to provide high quality of service (QoS) for different power applications services in particular. Although many approaches have been proposed to achieve high QoS in NAN, its personalized QoS requirements for diverse applications [12] are difficult to meet. For example, video monitoring, as a general service in daily life, is expected to balance including delay and packet loss rate [13]. Instead, for some other applications, their QoS requirements are to keep stable data transmission rates. Hence, there are great challenges to the SG NANs neighborhood area network because of the diverse heterogeneous applications and distinguished QoS requirements. There is an urgent demand for a controllable, flexible, and open business platform to realize a unified controlling and coordinated scheduling of the SG [14]. Moreover, it is critical to find a unified solution for the SG to improve the utilization and QoS simultaneously under a time-varying network. In this paper, we associate the QoS demand with the congestion control [15; 16; 17] to transform such a hard-evaluated metric into a more realistic one. A software defined network (SDN) [18; 19; 20] presents a solution by separating the function of the network controlling from data transmission and brings several advantages. On the one hand, the controlling plane is capable of optimizing networks by management to transfer all complex computational tasks (including QoS analysis and decisions) from hardware to a centralized control [21; 22]. On the other hand, the overall network latency has been improved and the data plane only focuses on the data processing [23; 24]. Thus, SDN is widely used as an improvement to the SG because of its flexibility. To further verify the feasibility of SDN in the power communication network, a research team [25] found that the centralized controllers of SDN can integrate the resources of the SG dynamically to make the network on-demand, flexible, and robust [26]. The flexible open interfaces in SDN can either realize the on-demand call for different services or handle the process of controlling by programming. The application of SDN in SG is described in Figure 2.  
Figure 1: The structure of smart grid.