Dynamic Routing Optimization Algorithm for Software Defined Networking

Abstract

Time and space complexity is the most critical problem of the current routing optimization algorithms for Software Defined Networking (SDN). To overcome this complexity, researchers use meta-heuristic techniques inside the routing optimization algorithms in the OpenFlow (OF) based large scale SDNs. This paper proposes a hybrid meta-heuristic algorithm to optimize the dynamic routing problem for the large scale SDNs. Due to the dynamic nature of SDNs, the proposed algorithm uses a mutation operator to overcome the memory-based problem of the ant colony algorithm. Besides, it uses the box-covering method and the k-means clustering method to divide the SDN network to overcome the problem of time and space complexity. The results of the proposed algorithm compared with the results of other similar algorithms and it shows that the proposed algorithm can handle the dynamic network changing, reduce the network congestion, the delay and running times and the packet loss rates.

Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

“…Afterwards, the scheme removed the affected forwarding rules { x fr } for the old flows in the switch Flowtable of the affected switches to clear space for new rules using R old . The procedure is demonstrated in Algorithm 1 line (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Secondly, a backup path will be computed for the affected flows.…”

“…Software Defined Networks (SDN) is an emerging network paradigm that fosters network innovation by separating the controller from the data plane using the OpenFlow standard [1]. This separation improved the network management, increased the chance of deploying innovations, and made the network more programmable [2,3]. However, networking equipment is failure-prone; therefore, some aspects such as reliability and fault management are necessary properties in the computer network.…”

Dynamic Routing and Failure Recovery Approaches for Efficient Resource Utilization in OpenFlow-SDN: A Survey

“…Although, the works in [74] [75], [76] may perform well in a small network, however, may not guarantee the same performance in a dynamic large-scale network, due to time and space complexity. In contrast, Hybrid Ant Colony Optimization (HACO) algorithm [77] was proposed to address the issue of time and space complexity. A box-covering and k-means clustering methods were used to divide the network into the small subnet.…”