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In the era of Internet of Things and 5G networks, handling real time network traffic with the required Quality of Services and optimal utilization of network resources is a challenging task. Traffic Engineering provides mechanisms to guide network traffic to improve utilization of network resources and meet requirements of the network Quality of Service (QoS). Traditional networks use IP based and Multi-Protocol Label Switching (MPLS) based Traffic Engineering mechanisms. Software Defined Networking (SDN) have characteristics useful for solving traffic scheduling and management. Currently the traditional networks are not going to be replaced fully by SDN enabled resources and hence traffic engineering solutions for Hybrid IP/SDN setups have to be explored. In this paper we propose a new Termite Inspired Optimization algorithm for dynamic path allocation and better utilization of network links using hybrid SDN setup. The proposed bioinspired algorithm based on Termite behaviour implemented in the SDN Controller supports elastic bandwidth demands from applications, by avoiding congestion, handling traffic priority and link availability. Testing in both simulated and physical test bed demonstrate the performance of the algorithm with the support of SDN. In cases of link failures, the algorithm in the SDN Controller performs failure recovery gracefully. The algorithm also performs very well in congestion avoidance. The SDN based algorithm can be implemented in the existing traditional WAN as a hybrid setup and is a less complex, better alternative to the traditional MPLS Traffic Engineering setup.

Node Disjoint Paths (NDP) is one of the extensively studied Graph Theory problem. In this problem, the input is a directed n vertex graph and the set of source destination pair of vertices. The goal is to find the maximum number of paths connecting each such pair, so that such discovered paths are node-disjoint. In this paper, a novel Canine Inspired Algorithm is proposed which is a bio-inspired one, based on the olfactory capabilities of canines in tracing and reaching a destination. Currently many of the existing algorithms try to identify disjoint paths in a linear manner, whereas the Canine algorithm can be executed in a concurrent manner, depending on the number of canines deployed to find the disjoint paths. The time complexity of the algorithm is estimated to be . We hope that this algorithm finds many applications in problems related to various fields such as communication networks, scheduling and transportation and provides better results.

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