A Clustering Approach to Edge Controller Placement in Software Defined Networks with Cost Balancing
This work addresses controller placement in 5G edge networks, offering improved performance for network operators, but it is incremental as it builds on existing clustering methods.
The authors tackled the Edge Controller Placement problem in 5G wireless networks by developing two deterministic annealing-based clustering algorithms, ECP-LL and ECP-LB, which outperform the state-of-the-art MINLP solver BARON in accuracy and speed while balancing synchronization and delay costs.
In this work we introduce two novel deterministic annealing based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks. These networks lie at the core of the fifth generation (5G) wireless systems and beyond. These algorithms, ECP-LL and ECP-LB, address the dominant leader-less and leader-based controller placement topologies and have linear computational complexity in terms of network size, maximum number of clusters and dimensionality of data. Each algorithm tries to place controllers close to edge node clusters and not far away from other controllers to maintain a reasonable balance between synchronization and delay costs. While the ECP problem can be conveniently expressed as a multi-objective mixed integer non-linear program (MINLP), our algorithms outperform state of art MINLP solver, BARON both in terms of accuracy and speed. Our proposed algorithms have the competitive edge of avoiding poor local minima through a Shannon entropy term in the clustering objective function. Most ECP algorithms are highly susceptible to poor local minima and greatly depend on initialization.