Optimizing Controller Placement for Software-Defined Networks
This addresses a key issue in software-defined networking for network engineers, but it is incremental as it builds on existing NP-hard problems by adding workload distribution considerations.
The paper tackles the controller placement problem in software-defined networks by simultaneously considering communication delay, control plane utilization, and controller workload distribution, developing a hybrid genetic algorithm and gradient descent method that effectively balances control plane utilization and network response time in simulations on small- and large-scale scenarios.
Controller placement problem (CPP) is a key issue for Software-Defined Networking (SDN) with distributed controller architectures. This problem aims to determine a suitable number of controllers deployed in important locations so as to optimize the overall network performance. In comparison to communication delay, existing literature on the CPP assumes that the influence of controller workload distribution on network performance is negligible. In this paper, we tackle the CPP that simultaneously considers the communication delay, the control plane utilization, and the controller workload distribution. Due to this reason, our CPP is intrinsically different from and clearly more difficult than any previously studied CPPs that are NP-hard. To tackle this challenging issue, we develop a new algorithm that seamlessly integrates the genetic algorithm (GA) and the gradient descent (GD) optimization method. Particularly, GA is used to search for suitable CPP solutions. The quality of each solution is further evaluated through GD. Simulation results on two representative network scenarios (small-scale and large-scale) show that our algorithm can effectively strike the trade-off between the control plane utilization and the network response time.