Software Defined Networking Controller Placement Research Articles

An assessment of nature-inspired metaheuristic algorithms for resilient controller placement in software-defined networks

Software-defined Networking (SDN) offers flexibility and programmability, making it a desirable option for modern network architecture. SDN provides numerous benefits to network administrators due to its centralized control architecture. This allows network administrators to manage and configure the network from a single location, making it easier to manage and automate network tasks. In a network of multiple controllers, the control plane’s resiliency may impact the overall system’s performance. In a controller failure scenario, switches must be reassigned to other active controllers with adequate capacity. Thus, we define a resilient controller placement (RCP) as an optimization problem. The aim is to design physically distributed and redundant controllers to manage switches with varying resilience levels. The propagation latency may increase due to the reassignment, increasing the network cost. The objective is to determine the number of controllers required, their positions, and the allocation of the network nodes to a particular controller while reducing the average propagation latency and cost in meeting the capacity constraint of the controller. Due to the wide area network (WAN) structure, four nature-inspired metaheuristic algorithms are proposed namely, simulated annealing (RCP-SA), moth-flame optimization algorithm (RCP-MFO), particle swarm optimization (RCP-PSO), and grey wolf optimization algorithm (RCP-GWO). These algorithms are evaluated on three network datasets to determine the optimum controller number and their positions. The experimental results show that RCP-GWO performs better than RCP-SA, RCP-MFO, RCP-PSO, and the Random methods.

Optimizing high availability multi-controller placement in SDN/NFV 5G networks: a survey

<p>In meeting the diverse and occasionally conflicting quality of service (QoS) requirements associated with modern communication networks, 5G technology has emerged as a pivotal player. In its architecture, 5G has adopted network function virtualization (NFV) and cloud-based approaches, aiming to simplify network and service deployment, operational processes, and management. The convergence of software defined networking (SDN) and NFV offers an effective solution, enabling scalable and high-performance 5G networks. However, this integration poses critical challenges, with the placement of SDN controllers being a central concern due to its significant impact on network performance, covering aspects such as latency, costs, and energy efficiency. This challenge is known as the controller placement problem (CPP). The central theme of this paper revolves around the intricate relationship between 5G core networks, virtualization technology, and the pressing concern of SDN controller placement, underscoring its significance in the modern networking landscape. We provide a survey of recent methodologies aimed at solving the CPP within the realm of SDN, with a particular focus on resiliency and high availability.</p>

ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks

Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP). CPP is an optimization problem that tries to minimize delay and achieve a balanced network. CPP becomes more important when we intend to increase reliability and reduce energy consumption. This paper proposes an Energy-efficient and Load-balanced optimization Algorithm for Reliable Controller Placement (ELA-RCP) in Software-Defined Networks (SDN). The proposed ELA-RCP is a three-step solution to CPPs. First, it determines the optimal number of controllers using the proposed cellular learning automata, taking into account network traffic status, scale, and budget. Second, an innovative meta-heuristic algorithm is proposed to identify proper controller locations based on factors like path reliability, controller processing capacity, propagation delay, and energy consumption. In the last step, load balancing and the reliability of the controllers are improved using queuing theory and a proposed greedy algorithm. ELA-RCP is evaluated with real-world topologies. The simulation demonstrates up to 25% improvement in energy consumption, up to 108% improvement in the network survivability, and up to 68% improvement in mean average delay rise compared to particle swarm optimization, firefly optimization algorithm, ant lion optimization, and seagull optimization algorithm.

PUAL-DBSCP: Personalized Ubiquitous Adaptive Learning for Density-Based Splitting Controller Placement in software-defined networks

In the field of personalized and adaptive education, ubiquitous adaptive learning (PUAL), which aims to give students customized learning experiences to improve their educational achievements, has grown in importance. The difficulties of implementing PUAL in the setting of Software Defined Networks (SDN) are addressed in this study. Using a capacity-based switch-splitting technique, DBSCP separates the SDN into several sub-networks. One controller is thoughtfully positioned within each of the sub-networks, which are each thought of as separate domains. This study examines the creation and use of DBSCP, emphasizing how it could revolutionize how educational content is distributed and personalized in software-defined networks. This research introduces a novel method for individualized ubiquitous adaptive learning called Density-Based Splitting Controller Placement (PAUL-DBSCP). The size of each sub-network in the proposed technique can be determined by the controller’s bandwidth and density-based splitting results based on identifying the ideal number of controllers. On a collection of 262 network topologies that are available to the public, the performance is accessed for the proposed technique. The performance evaluation of $ method, CNPA and SAPKM state-of-the-art approaches has been analyzed with the proposed approach and it has been observed that the results achieve 4.2% worst-case delay with regard to the number of controllers.

A novel framework for capacitated SDN controller placement: Balancing latency and reliability with PSO algorithm

Software Defined Networking (SDN) is a new network architecture theoretical framework, which based on the partition of the control plane and the data plane. Some of the important features of SDN are centralized network management, open code, scalability, effective utilization of network resources, effective traffic management, security etc. All these administrative decisions taken by the controller which lies at the top of the data plane. Efficient placing of controllers is essential to enhance the network’s reliability, which ultimately improves the performance of the network. Much of the work done in the SDN has been aimed on controller placement problems (CPP) by considering latency and reliability of the network separately whereas few of them acknowledged both latency and reliability simultaneously. We considered both communication latency and reliable communication to solve CPP. To achieve this, we aim to minimize the total average latency of the network with a reliable network model that can work perfectly up to the failure of n-1 controllers out of n deployed. We first formulate an efficient algorithm “Capacitated Controllers Arrangement (CCA)” to define the problem and used nature-based metaheuristic technique, Particle Swarm optimization (PSO) to achieve the solution. By intelligently allocating controllers, this dynamic system minimizes network latency and optimizes switch assignments. Three controllers deliver excellent reliability and latency performance even despite controller breakdowns, according to experiments conducted on the Internet2 OS3E architecture. This novel method not only increases SDN efficiency but also establishes a standard for further improvements in controller placement tactics. The outcome shows that the proposed approach achieves its goal efficiently and in future other multi-objective optimization techniques will be explored to solve reliability aware CPP.

Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach

The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks.

Dynamic SDN Controller Placement based on Deep Reinforcement Learning

Software-defined Networking (SDN) is a revolutionary network architecture whose benefits stem partly from separating the data plane and control plane. In this scheme, the control functionalities are relocated to a logically centralized SDN controller which makes efficient and globally optimal forwarding decisions for network devices. Despite the fact that network virtualization technologies enable elastic capacity engineering and seamless fault recovery of the SDN controller, an optimal controller placement strategy that can adapt to changes in networks is an important but underexplored research topic. This paper proposes a novel deep reinforcement learning-based model that dynamically and strategically adjusts the location of the controller to minimize the OpenFlow latency in a virtualized environment. The experimental results demonstrate that the proposed strategy out performs both a random strategy and a generic strategy. Furthermore, this paper provides detailed instructions on how to implement the proposed model in realworld software-defined networks.

Distributed Controller Placement in Software-Defined Networks with Consistency and Interoperability Problems

Software-defined networking (SDN) brings an innovative approach to networking by adopting a flow-centric model and removing networking decisions from the data plane to provide them centrally from the control plane. A single centralized controller is used in a traditional SDN design. However, the complexity of modern networks, due to their size and requirements’ coarseness, has made using a single controller a source of performance bottlenecks. Similarly, the solution found by using multiple controllers in distributed control planes brings forth the profound issue of interoperability, consistency, and the “controller placement problem” (CPP). It is an NP-hard problem that deals with positioning controllers at optimum locations within the network and mapping with resources at the data plane to meet quality of service (QoS) requirements. Over the years, the problem has received significant attention from the research community, and many solutions have been considered. This paper offers an in-depth review of the proposals by providing an updated evolution of the problem concerning the application environment, design objectives, and cost and controller type. Based on our findings, new research ideas were identified and discussed.

A co-evolutionary genetic algorithm for robust and balanced controller placement in software-defined networks

The controller placement problem (CPP) is one of the main issues that need to be addressed in the context of Software Defined Networking (SDN), especially when different aspects are being considered, such as latency, capacity, reliability, and load balancing. Most of the solutions in the literature address these aspects by considering a fixed load for each controller and attempting to equally distribute the traffic demand of the switches among the controllers, which also have a fixed common capacity. On the contrary, in this work, the CPP is studied by considering load, controller capacity, and the failure probability of controllers and links as varying over time. The CPP is formulated in terms of a robust optimization problem, which, by introducing the concept of scenario, takes into account changes in the network status due to failures, load variations, and changes in switches’ demand and controllers’ capacity. The provided solution is robust, that is, neither controllers’ re-placement nor switches’ re-assignment is required as network conditions change. Besides, a co-evolutionary algorithm is provided to solve the aforementioned optimization problem. Two populations coevolve based on the concept of complementary evolution of allied species in nature. Experimental results on a set of real-world network topologies and comparisons with the state-of-the-art have proven the superiority of the proposal, in terms of better latency, load balancing and resilience, in solving the CPP under different network status changes that might occur over time.

A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks

A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks

Multi-Objective Controller Failure Aware Capacitated Controller Placement in Software-Defined Networks

Software-Defined Networking disassociates the control plane from data plane. The problem of deciding upon the number and locations of controllers and assigning switches to them has attracted the attention of researchers. Foreseeing the possibility of failure of a controller, a backup controller has to be maintained for each switch so that the switches assigned to the failed controller can immediately be connected to their backup controllers. Hence, the switches cannot experience disconnections in case of failure of their controller. In this paper, two mathematical models are proposed. The first model focuses on minimizing the average of latencies from all switches to their backup controllers while considering the failure of the controllers. The second model aims at minimizing both the average and worst-case of latencies from all switches to the corresponding backup controllers. Both of our models are evaluated on three networks and are compared (in terms of two metrics, viz., average and worst-case latencies) with an existing model that focuses on minimizing only worst-case latency. The first model gives better average latency compared to the reference model. The second model also gives better average latency and almost equal worst-case latency compared to the reference model.

An Effective Approach for Controller Placement in Software-Defined Internet-of-Things (SD-IoT).

The Software-Defined Networking (SDN) paradigm has transferred network intelligence from network devices to a centralized controller. Controllers are distributed in a network to eliminate a single point of failure (SPOF) and improve reliability and balance load. In Software-Defined Internet of Things (SD-IoT), sensors exchange data with a controller on a regular basis. If the controllers are not appropriately located in SD-IoT, the E2E latency between the switches, to which the sensors are connected, and the controller increases. However, examining the placement of controllers in relation to the whole network is not an efficient technique since applying the objective function to the entire network is a difficult operation. As a result, segmenting the network into clusters improves the efficiency with which switches are assigned to the controller. As a result, in this research, we offer an effective clustering strategy for controller placement in SDN that leverages the Analytical Network Process (ANP), a multi-criteria decision-making (MCDM) scheme. The simulation results demonstrated on real Internet topologies suggest that our proposed method outperforms the standard k-means approach in terms of E2E delay, controller-to-controller (C2C) delay, the fair allocation of switches in the network, and the communication overhead.

Budget-Constrained Controller Placement in Software-Defined Networking

Budget-Constrained Controller Placement in Software-Defined Networking

Controller Placement in SDN with Low Latency Using Meta-heuristic Algorithms

Software-Defined networks (SDNs) are a new generation of computer networks that have eliminated many of the problems of traditional networks. These networks use a three-tier architecture in which the physical layers, controller, and management are located at different levels. This new architecture has made the network very dynamic, and many of the previous problems in the network have been solved. As the size of the network increases, using a controller across the network will cause issues such as increasing the average latency between the switches and the controller, as well as forming a bottleneck in the controller. For this reason, it is recommended to use multiple physical controllers on the control plane. Due to the cost of purchasing and maintaining the controller, it is necessary to solve the mentioned problem with the least controllers. The question is, to achieve a goal such as reducing latency to an acceptable threshold, at least how many controllers are needed, where the controllers should be located, and which switches should be monitored by which controller? Since this is an NP-Hard problem, methods based on meta-heuristic algorithms can be effective in solving it. In this article, we have solved the problem of controller placement in software-based networks to reduce latency using the cuckoo meta-heuristic algorithm. The simulation results show that the efficiency of our proposed method is between 16 to 70 percent better than the method proposed by the PSO algorithm.

Multi-Objective Control Plane Dimensioning in Hybrid SDN/Legacy Networks

Software defined networking (SDN) is gaining the confidence of network operators, who are increasingly motivated to introduce it in their networks. However, since SDN is based on centralized control plane (decoupled from the data plane), it is incompatible with distributed control plane of legacy networks. As adopting SDN by complete overhaul of existing legacy networks is infeasible, several solutions propose to incrementally adopt SDN in legacy networks, resulting in hybrid SDN/legacy networks during the transition phase. While strategies to effectively introduce SDN switches in legacy networks have received significant attention in the literature, the same is not true for the associated SDN controller placement in hybrid SDN networks. In this paper, we introduce and formulate the multi-objective control plane dimensioning problem in hybrid SDN/legacy networks, considering network resilience, flow-setup latency and controller load balancing as objectives. We propose optimization models for both the single-objective as well as the multi-objective controller placements. We model both controller processing latency (based on queueing theory) and inter-controller network state synchronization latency (based on Steiner trees) without compromising linearity of the optimization formulations. A genetic algorithm-based heuristic is presented to efficiently deduce the approximate Pareto frontier for our problem. Extensive simulations over a large number of real networks establish the effectiveness of our approach in terms of several single-objective and multi-objective performance metrics.

Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

SDN Controller Placement With Availability Upgrade Under Delay and Geodiversity Constraints

An inherent problem in Software-Defined Networking (SDN) is the Controller Placement Problem, which addresses how many controllers to deploy in the network, and where to place them. Several variants of this problem have been addressed and researched to find the placements that adapt best to different contexts. In this article, we address a more complex variant of this problem, to satisfy QoS requirements and to offer robustness against disaster-based failures. We address the joint optimization problem of controller placement and finding a tree subgraph which can be upgraded to have enhanced availability, in order to satisfy delay and availability constraints. Additionally, we consider geodiversity constraints as a way to enhance robustness to disaster-based failures.

A new GSO based method for SDN controller placement

In the context of wide area networks (WANs) and software defined networking (SDN), reducing the communication delays experienced by the network devices is an important challenge whose solution requires a careful placement of controllers to decrease the end-to-end latencies. Although the majority of studies focusing on the controller placement problem (CPP) only consider the switch–controller propagation delays and inter-controller latencies, the controllers’ capacity needs to be addressed for lowering the end-to-end delays. While the controllers with a variety of processing rates, number of ports, and costs are available in the market, Internet Service Providers (ISPs) need to consider the affordability and capability of compensating their needs by maximizing the use of their networks’ resources. To propose a solution for this important problem, we consider the Knapsack 0–1problem and formulate the Garter Snake Optimization Capacitated Controller Placement Problem (GSOCCPP), a meta-heuristic algorithm, with new iterations and temperate mating conditions to solve the CPP. This algorithm uses a reasonable amount of computation time to obtain the minimum delays. A number of Topology-Zoo datasets are analyzed with a variety of small to large scale data plane nodes to evaluate the GSOCCPP algorithm based on different controllers’ capacities. The simulation results demonstrate that, in addition to outperforming similar meta-heuristic and clustering algorithms such as the Firefly Algorithm, Particle Swarm Optimization, and the k-means++, our newly proposed GSOCCPP algorithm is successful in achieving the lowest execution time among the analyzed algorithms. Furthermore, this proposed solution has a more efficient memory consumption compared to other algorithms for controller placement in different network topologies.

Controller placements for latency minimization of both primary and backup paths in SDNs

Software-defined networking (SDN) is a revolutionary network architecture that separates the network control layer from the underlying equipment. Multiple controllers form a logically centralized control layer in large-scale networks, which raises the controller placement problem. Most of the research on latency-oriented controller placement optimized the delay between switches and controllers assuming the network is reliable. However, the network is subject to link failures. In this paper, we formulate a novel multi-objective SDN controller placement problem with the aim to minimize the switch-to-controller communication delay for both the cases without link failure and with single-link-failure. We propose an efficient metaheuristic-based Reliability-Aware and Latency-Oriented controller placement algorithm (RALO) for multi-objective multiple controller placements. The algorithm constructs an initial feasible solution by a greedy method with network partition, then repeatedly generates new solutions with variable neighborhood search. Once a new solution is generated, the algorithm decides whether to accept the new solution as a non-dominated solution to the problem and performs update operation on the Pareto optimal solution set. Meanwhile, to avoid falling into the local optimum, the algorithm also performs perturbation and destruction operations on the current solution. We finally conduct experiments through simulations on 8 real network topologies and two kinds of generated networks conforming to ER (Erdos–Renyi) random model and small-world model. Experimental results demonstrate that the proposed algorithm can achieve a competitive performance of switch-to-controller latencies in both the cases without link failure and with single-link failure, and the accumulated delay of primary and backup paths between the controllers and the switches. The Pareto optimal solution set provided by algorithm RALO allows network administrators with flexible choices to strike a trade-off between the switch-to-controller delay of primary and backup paths.

An Efficient Approach to Robust SDN Controller Placement for Security

Security is one of the critical issues in traditional networks. Software-Defined Networking (SDN) improves the security aspect by separating the control plane and the data plane of networks. To improve the performance of SDN, researchers have designed many advanced controller prototypes and considered the controller placement problem. However, link failures are critical security issues in networks and greatly impact SDN’s security. The controller placement problem for link failures is still challenging today. In this paper, we study the SDN controller placement problem for single-link and multi-link failures, respectively. For single-link failures, we develop a heuristic algorithm to address the controller placement problem. For multi-link failures, we introduce the Monte Carlo Simulation to reduce the computational overhead. We conduct experiments with real network topologies, and the simulation results show that the heuristic algorithm can save significantly more time than the optimal algorithm, while achieving good performance.