Significant research has been carried out in the recent years for generating systems exhibiting intelligence for realizing optimized routing in networks. In this paper, a grade based twolevel based node selection method along with Particle Swarm Optimization (PSO) technique is proposed. It assumes that the nodes are intelligent and there exist a knowledge base about the environment in their local memory. There are two levels for approaching the effective route selection process through grading. At the first level, grade based selection is applied and at the second level, the optimum path is explored using PSO. The simulation has been carried out on different topological structures and it is observed that a graded network produces a significant reduction in number of iteration to arrive at the optimal path selection.

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Abstract-Software Defined Networking is a paradigmshifting technology in the field of computer networking. It empowers network administrators by giving them the ability to manage the network services through abstraction of the low-level network functionalities. This technology simplifies networking and makes it programmable. This paper presents an implementation of this new paradigm of networking, which can replace the currently existing legacy networking infrastructure to provide more control over the network, perform a better analysis of the network operation and hence program the network according to the needs of the network administrator. This implementation also empowers the network administrators to provide Quality of Service to its users that are connected to the network and uses the services of the network. Therefore, it benefits both the network administrator and the users. Also, the ping latency in the network is reduced by 5-10%, and the number of packets in is reduced by 60-70% in the solution developed depending on the size of the network.

Abstract-In this paper we compare the two intelligent route generation system and its performance capability in graded networks using Artificial Bee Colony (ABC) algorithm and Genetic Algorithm (GA). Both ABC and GA have found its importance in optimization technique for determining optimal path while routing operations in the network. The paper shows how ABC approach has been utilized for determining the optimal path based on bandwidth availability of the links and determines better quality paths over GA. Here the nodes participating in the routing are evaluated for their QoS metric. The nodes which satisfy the minimum threshold value of the metric are chosen and enabled to participate in routing. A quadrant is synthesized on the source as the centre and depending on which quadrant the destination node belongs to, a search for optimal path is performed. The simulation results show that ABC speeds up local minimum search convergence by around 60% as compared to GA with respect to traffic intensity, and opens the possibility for cognitive routing in future intelligent networks.

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