# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 2 Related Works  
With the combination of SG and new energy, research on various aspects of the power grid are increasing. Based on the high-dimensional random matrix theory, [PERSON] et al. [28], analyzed the spectral distribution of eigenvalues of the high-dimensional random matrix covariance matrix for massive power consumption data. Based on the statistical characteristics of the random matrix, a method of power status recognition based on  
Figure 2: Application of SDN in SG. By applying programmable SDN Controllers, the communication resource in SG will be dynamically rescheduled. the big data of power consumption is proposed. Although this method can meet the requirements of visualization, timeliness, reliability, and security of the power grid, it focuses on the power consumption of the grid and does not consider the service quality requirements of different business flows in the SG, and the communication quality is not guaranteed. SG communication networks must support a variety of different QoS requirements of the application. The current network infrastructure cannot dynamically adapt to the requirements of various applications in the SG [29]. In order to solve this problem, [PERSON] et al. [30] presented an SDN application in the SG, and designed a system architecture for the SG environment. For those SG frameworks supporting SDN, they are beneficial to process flows in micro size. [PERSON] et al. [31] proposed a mechanism, by deploying OpenFlow technology on SG, to provide automatic fault recovering, load balancing, and QoS guarantee. The algorithm creates two virtual network interfaces to isolate high-priority data streams and best-effort data streams. The data rate of the virtual interface is dynamically configured according to the queue size and can control traffic in the network. For the SDN network, based on the characteristics of the global management of the controller in the OpenFlow network, it can not only meet the requirements of different QoS, but also can better monitor the resources of the network. At present, the research on the congestion management mechanism under the OpenFlow network framework has had some achievements. [PERSON] et al. designed a LOBUS algorithm for the SDN network, which simply applies the greedy selection strategy to select the path with the least response time [32]. However, this solution that attempts to use a comprehensive load-balancing algorithm ignores the unpredictable changes in load state, and when the network grows more and more, the network conditions are less controllable. On this basis, [PERSON] et al. in [33] introduced a new dynamic balance flow-routing algorithm, namely LABERIO, to monitor the distribution of traffic in the network. LABERIO sets a congestion threshold, which finds the most congested links in the network according to thresholds. Then, it finds the data stream which occupies the maximum bandwidth of the link to redirect routing. Although the LABERIO algorithm prevents the occurrence of network congestion with a certain probability, it only considers the data flow bandwidth parameters, ignoring the requirements of other business attributes. Thus, the network sensitivity and accuracy can not be guaranteed. [PERSON] et al. proposed an algorithm, OFFICER, for the path allocation linear optimization model, which creates a default route for all communications [34]. After that, it introduces some deviations from this path and utilizes those deviations to arrive at the destination through different strategies. However, refs. [35; 36], when the data in the network is higher, the table items in the flow table will also increase correspondingly, and the use of this algorithm can reduce the service quality of the packets. However, existing approaches have ignored either the dynamic changes of congestion or personalized QoS demands. In addition, the existing solution for such optimization problem is not optimal.