A Scalable Approach to SDN Control Plane Management: High Utilization Comes With Low Latency

Abstract

One major research challenge for Software-Defined Networking is to properly deploy and efficiently utilize multiple controllers to improve resource utilization and maintain high network performance. While addressing this Controller Placement Problem (CPP), many existing studies overlooked the importance and influence of the Controller Scheduling Problem (CSP) with the central focus on proper distribution of requests from all switches among all controllers. In this paper, we define a new Controller Placement and Scheduling Problem (CPSP), emphasizing on the necessity and importance of tackling both CPP and CSP simultaneously in a coherent framework. To solve CPSP, we must seek a combination of solutions to both problems. Particularly, CSP is addressed based on a given solution to CPP and a Gradient-Descent-based (GD-based) scheduling algorithm is developed to optimize the probabilistic distribution of requests among all controllers. Built on the GD-based approach for controller scheduling, a Clustering-based Genetic Algorithm with Cooperative Clusters (CGA-CC) is further proposed to address CPP. In comparison to the majority of heuristic methods developed in the past, CGA-CC has two unique strengths. Specifically, it partitions a large network to substantially reduce the search space of the Genetic Algorithm (GA), resulting in fast identification of high-quality CPP solutions. Moreover, a greedy load re-distribution mechanism is developed to handle unexpected demand variations by dynamically forwarding bursting requests to neighboring sub-networks. Extensive simulations showed that our algorithms can significantly outperform several existing algorithms, including a recently proposed approach called Multi-controller Selection and Placement Algorithm (MSPA), in terms of both response time and controller utilization.