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Software-defined networking (SDN) separates the forwarding plane and control plane of the network equipment, adopts a centralized control mode to simplify network deployment and improve network management efficiency and realizes the network flexible control and management of traffic through programmable open interfaces. At present, it has been widely used in domestic and international data centre networks. With the explosive growth of the scale of data centres and the increase in user requirements for service quality, load balancing and congestion control of data centres have become significant issues in current research. After studying and analysing data centre SDN architecture and load balancing problems in detail, certain experts proposed an SDN load balancing algorithm, based on improved ant colony optimization-load balancing (IACO-LB). Firstly, the overall framework of SDN load balancing in data centres is studied, which is mainly divided into three parts: basic network equipment, OpenFlow protocol and controller. Among them, the controller constitutes the core of the entire load balancing system, including four modules: network topology awareness, status collection, the core of the load balancing algorithm and the flow table distribution. Then, an SDN load balancing algorithm, based on the improved ant colony optimization (IACO) is proposed to achieve dynamic load balancing of the SDN. The algorithm fully considers the performance parameters of network links and servers, and its design is based on the principle of selecting links and servers with low utilization. The evaluation methods of server module and link module are designed and the ant colony algorithm is used to find the global optimal solution. In order to prevent the algorithm from falling into local optimum, the Kent chaotic model is adopted to disturb the transition probability of the ant colony, by improving the basic ant colony algorithm. Finally, a network topology model was established in MATLAB to carry out simulation experiments. The results show that, compared with the equivalent multi-path algorithm and path server traffic scheduling algorithm, IACO-LB can effectively solve the load balancing problem of SDN and can dynamically adjust the routing scheme, according to the changes in network link traffic and server utilization. The algorithm converges quickly and can achieve a better global load balancing scheme. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, network attacks occur frequently and numerous hidden dangers appear in network security. Therefore, improving the performance of intrusion detection systems to detect and defend against attacks is the key to ensuring network security. However, in the face of complex and massive network data feature information, traditional machine learning methods suffer from data imbalance and feature redundancy, which results in low detection rates, high false alarm rates and poor real-time performance of intrusion detection systems. Therefore, to address these problems, this paper proposes a data imbalance-based Convolutional Neural Network Intrusion Detection Method (CNN-IDMDI). First, an oversampling method is used to solve the data imbalance problem by decomposing the increased number of samples for the few attacks with multiple sampling to form multiple sub-samples. Second, the gradient coordination mechanism and the improved loss function Focal Loss are combined to calculate the loss between the actual and expected values to detect network malicious attacks in high-dimensional and unbalanced data. Finally, the methods in this paper are compared with the current mainstream intrusion detection methods on the NSL-KDD dataset for binary and multi-classification detection. The experimental results show that the method in this paper can effectively improve the effectiveness of CNN intrusion detection and network anomaly. The average accuracy of the CNN intrusion detection method based on data imbalance for binary intrusion detection is 98.73% and the implementation time of the method is 1.42 s, which is 15.45%, 12.76%, and 2.91% higher than the average accuracy of the CNN, the CNN Long Short-Term Memory (CNN-LSTM) and the CNN Neural-induced Support Vector Machine (CNN-NSVM) methods, respectively, and the detection time is saved by 0.82 s, 0.72 s, and 0.61 s, respectively. The average accuracy of the CNN intrusion detection method based on data imbalance for multi-classification intrusion detection is 94.55% and the time required to complete the detection is 2.96 s. This improves the average accuracy by 16.09%, 12.71%, and 3.66% compared with the CNN, CNN-LSTM and CNN-NSVM methods, respectively. It is also quicker, as the time consumption of CNN is 8.84 s, CNN-LSTM is 8.31 s and CNN-NSVM is 6.43 s. Therefore, the CNN-IDMDI method for intrusion detection proposed in this paper has higher accuracy and faster speed. [ABSTRACT FROM AUTHOR]

Course scheduling is a combinatorial optimization problem with multiple constraints. To achieve the reasonable allocation of teaching resources, all courses should be arranged under a number of specified constraints. With the increased number of courses, the solution space increases exponentially. However, the traditional methods cannot quickly find the optimal solution. In order to overcome the disadvantages of low efficiency and the possibility of high conflict in traditional course scheduling, this research study proposes a course scheduling, method based on an improved binary cuckoo search algorithm (IBCS). First of all, a multi-objective and multi-constraint mathematical model of course scheduling is established. Course scheduling involves issues, such as allocating teachers, courses, classes, classrooms and time under specific constraints. The class element is defined to represent the class and the course that the teachers will teach. Therefore, the problem of course scheduling is transformed into the process of mapping from the class element to the classroom-time pair. Six hard constraints and three soft constraints are also defined accordingly. Then, the BCS algorithm is used to search the best course scheduling scheme in the binary-encoded solution space. In order to control the convergence rate effectively and avoid falling into the local optimum, a dynamically adjustable, equilibrium coefficient is introduced. Therefore, the algorithm retains the diversity of the solutions while converging. Finally, this research study constructs the data set and carries out the simulation experiment. It also compares the IBCS algorithm in this paper with the genetic algorithm and the standard binary cuckoo search algorithm (BCS). Experimental results show that the IBCS algorithm can converge in effective time. Meanwhile, it can achieve an improved global scheduling scheme with higher stability. [ABSTRACT FROM AUTHOR]