Heuristic approaches for the reliable SDN controller placement problem

Abstract

Software‐defined networking (SDN) is one of the most used network architecture that divides the forwarding plane and control plane. The SDN centrally observes and regulates the network through a software control in the control plane, called as controller. Multiple controllers are needed to manage the software‐defined WAN (SD‐WAN) for handling the scalability and reliability issues of the network as one controller is not enough. Deploying numerous controllers efficiently to improve the performance of the network is known as controller placement problem (CPP). This paper proposes a Varna‐based optimization (VBO) for a reliable CPP that minimizes the total average latency of SDN. To the best of our knowledge, the proposed work is a novel approach, which is compared with particle swarm optimization, teacher learning‐based optimization, and Jaya algorithms to solve reliable CPP. The experimental results show that VBO gives better performance than teacher learning‐based optimization (TLBO), PSO, and Jaya algorithms for the popular topologies that are publicly available.

Legacy Network Integration with SDN-IP Implementation towards a Multi-Domain SoDIP6 Network Environment

“…The number of controllers required to be assigned to switches and their proper placement in SDN play an important role in achieving considerable flow setup time with better fault handling and effective controller load balancing [26]. Sufficient studies [26][27][28][29][30] using different techniques are available regarding the controller placement in pure SDN [26,27]. One of our concerns in this study was to find a proper location for a master controller during carrier-grade service provider network migration.…”

Parameter Optimization Model of Heuristic Algorithms for Controller Placement Problem in Large-Scale SDN

“…To ensure consistency between the controllers in the SDN, communication between the controllers is also required. Therefore, the delay between the controllers is also a key CPP indicator [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Besides, placement cost [21], network fault tolerance [28], path failure probability [30] and the like, are common CPP indicators.…”

“…These clustering-based controller placement algorithms first divide the entire SDN into multiple subnets and then determine the location of each controller to minimize the delay between the controllers and the switches. However, more controller placement algorithms were designed based on the heuristic algorithm, for example, the controller placement algorithm based on the physical law heuristic algorithm: simulated annealing algorithm [15]; the controller placement algorithm based on the swarm intelligent behavior heuristic algorithm, which includes the PSO [18], FA [25], moth flame optimization [27], and adaptive bacterial foraging optimization [30]; the controller placement algorithm based on the human behavior heuristic algorithm, which includes TLBO [20] and VBO [29]; and the controller placement algorithm based on the evolutionary behavior heuristic algorithm, which includes non-dominated sorting genetic algorithm [17], nondominated sorting genetic algorithm II [22], and genetic algorithm [32]. The controller placement algorithm based on the heuristic algorithm usually simulates the coordinates of the controllers which need to be placed in the network into a multi-dimensional object in the heuristic algorithm, uses the location update strategy, sets the objective function, and iteratively solves the CPP.…”

“…The PSO in [18], the TLBO in [20], the FA in [25], the VBO in [29], and the BA with unoptimized parameters were compared with the parameter-optimized BACPA, FACPA, and VBOCPA. It should be noted that the PSO can be applied not only to the SDCPM, but also to the POM.…”

“…For example, Particle swarm optimization (PSO) is inspired by ethnic behavior of birds [9]; Bat algorithm (BA) is inspired by foraging behavior of bats [10]; Firefly algorithm (FA) is inspired by courtship behavior of fireflies [11]; Teaching-learningbased optimization (TLBO) is inspired by teaching behavior of human beings [12]; and varna-based optimization (VBO) is inspired by social structure and production behavior of human beings [13]. As a result, the heuristic algorithm is more and more used for solving the CPP due to its low-time complexity and its suitability for solving high-dimensional problems [17][18][19][20][21][22][23][24][25].…”