Dynamic optimization scheme for load balancing and energy efficiency in software-defined networks utilizing the krill herd meta-heuristic algorithm

Forghani, Mohammadreza; Soltanaghaei, Mohammadreza; Zamani Boroujeni, Farsad

doi:10.1016/j.compeleceng.2023.109057

Cited by 7 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

Mohanty,

Sahoo

2024

Concurrency and Computation

View full text Add to dashboard Cite

SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

show abstract

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

Mohanty,

Sahoo

2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Optimizing Cloud Task Scheduling Through Innovative Metaheuristic Algorithm and Impulsive Fuzzy C-Means

Jhingran,

Ahateshaam,

Singh

2024

Information Systems Engineering and Management

View full text Add to dashboard Cite

Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing

Zhou,

Yang,

et al. 2024

Computer Communications

View full text Add to dashboard Cite

Dynamic optimization scheme for load balancing and energy efficiency in software-defined networks utilizing the krill herd meta-heuristic algorithm

Cited by 7 publications

References 18 publications

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

Optimizing Cloud Task Scheduling Through Innovative Metaheuristic Algorithm and Impulsive Fuzzy C-Means

Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing

Contact Info

Product

Resources

About