| Abstract: | Software-defined networking is an emerging paradigm for supporting flexible network management. In the traditional architecture for a software-defined network (SDN), the controller commonly uses a general routing algorithm such as Open Shortest Path First (OSPF), which chooses the shortest path for communication. This may cause the largest amount of network traffic, especially in large-scale environments. In this paper, we present the design for a novel SDN-based four-tier architecture for scalable secure routing and load balancing. In Tier 1, user authentication is conducted using elliptic curve cryptography (ECC); this avoids unnecessary loads from unauthorized users. In Tier 2, packet classification is performed based on the packet characteristics using the fuzzy analytical hierarchy process (fuzzy AHP), and packets are placed into three individual queues. In Tier 3, scalable secure routing is achieved by selecting the optimal path using the improved particle swarm optimization and ant colony optimization algorithms. With these optimization algorithms, we can adaptively change the number of users, the number of switches, and other parameters. In Tier 4, the recommended secure cluster (multicontroller) management is accomplished using an algorithm that employs modified k-means clustering and a recurrent neural network. Deep reinforcement learning (DRL) is also proposed for updating the controller information. Experimental results are analyzed using the OMNeT++ network simulator, and the evaluated performance displayed improvement over a variety of existing methods in terms of response time (50% to 60%), load (55%), execution time (3.2%), throughput (9.8%), packet loss rate (1.02%), end-to-end delay (50%), and bandwidth consumption (45%). |
