Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Abstract

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

“…To develop solutions that are effectively scalable and tailored to the uses of UAVs, it is necessary to adopt new methods of thinking about these issues. Such issues use advancements in AI, edge computing, and communication technology 6 . Cooperation between researchers, industry stakeholders, and regulatory authorities is required to overcome these obstacles and fully exploit the potential of edge computing driven by AI for optimizing UAV operating procedures 7 .…”