Distributed Software Defined Networking Controllers Research Articles

A Blockchain-Based Security Framework for East-West Interface of SDN

Software-Defined Networking (SDN) has emerged as a revolutionary architecture in computer networks, offering comprehensive network control and monitoring capabilities. However, securing the east–west interface, which is crucial for communication between distributed SDN controllers, remains a significant challenge. This study proposes a novel blockchain-based security framework that integrates Ethereum technology with customized blockchain algorithms for authentication, encryption, and access control. The framework introduces decentralized mechanisms to protect against diverse attacks, including false data injection, man-in-the-middle (MitM), and unauthorized access. Experimental results demonstrate the effectiveness of this framework in securing distributed controllers while maintaining high network performance and low latency, paving the way for more resilient and trustworthy SDN infrastructures.

Request Dispatching Over Distributed SDN Control Plane: A Multiagent Approach.

Software-defined networking (SDN) allows flexible and centralized control in cloud data centers. An elastic set of distributed SDN controllers is often required to provide sufficient yet cost-effective processing capacity. However, this introduces a new challenge: Request Dispatching among the controllers by SDN switches. It is essential to design a dispatching policy for each switch to guide the request distribution. Existing policies are designed under certain assumptions, including a single centralized agent, global network knowledge, and a fixed number of controllers, which often cannot be satisfied in practice. This article proposes MADRina, Multiagent Deep Reinforcement Learning for request dispatching, to design policies with high dispatching adaptability and performance. First, we design a multiagent system to address the limitation of using a centralized agent with global network knowledge. Second, we propose a Deep Neural Network-based adaptive policy to enable request dispatching over an elastic set of controllers. Third, we develop a new algorithm to train the adaptive policies in a multiagent context. We prototype MADRina and build a simulation tool to evaluate its performance using real-world network data and topology. The results show that MADRina can significantly reduce response time by up to 30% compared to existing approaches.

Coalitional Game Strategy and TSSM for Efficient Load Balancing in Software Defined Networking

Using distributed SDN control, software defined networking (SDN) delivers additional flexibility to network management, and it has been a significant breakthrough in network innovation. Switch migration is often used for distributed controller workload balancing. The time-sharing switch migration (TSSM) scheme proposed a strategy in which multiple controllers are allowed to share the workload of a switch via time sharing during an overloaded condition, resulting in reduced ping-pong controller difficulty, fewer overload occurrences, and improved controller efficiency. However, it requires more than one controller to accomplish, it has greater migration costs and higher controller resource usage during the TSSM operating time. As a result, we presented a coalitional game strategy that optimizes controller selection throughout the TSSM phase depending on flow characteristics. The new TSSM method reduces migration costs and controller resource usage while still providing TSSM benefits. For the sake of practicality, the proposed strategy is implemented using an open network operating system. The experimental findings reveal that, as compared to the typical TSSM system, the proposed technique reduces migration costs and controller resource usage by approximately 18%.

Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

The deployment of distributed multi-controllers for Software-Defined Networking (SDN) architecture is an emerging solution to improve network scalability and management. However, the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low resilience. Thus, we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane failures. This fault-tolerance strategy has been applied to distributed SDN control architecture, which allows each switch to migrate to next controller to enhance network performance. In this paper, the Reliable and Dynamic Mapping-based Controller Placement (RDMCP) problem in distributed architecture is framed as an optimization problem to improve the system reliability, quality, and availability. By considering the bound constraints, a heuristic state-of-the-art Controller Placement Problem (CPP) algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular controllers. The algorithm identifies the optimal controller location, minimum number of controllers, and the expected assignment costs after failure at the lowest effective cost. A metaheuristic Particle Swarm Optimization (PSO) algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and cost-effectiveness. The effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline algorithms. The findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

An Efficient Switch Migration Scheme for Load Balancing in Software Defined Networking

Software-defined networking (SDN) provides increased flexibility to network management through distributed SDN control, and it has been a great breakthrough in network innovation. Switch migration is extensively used for workload balancing among distributed controllers. The time-sharing switch migration (TSSM) scheme proposes a strategy in which more than one controller is allowed to share the workload of a switch via time sharing during overloaded conditions, resulting in the mitigation of ping-pong controller difficulty, a reduced number of overload occurrences, and better controller efficiency. However, it has increased migration costs and higher controller resource consumption during the TSSM operation period because it requires more than one controller to perform. Therefore, we have proposed a strategy that optimizes the controller selection during the TSSM period based on flow characteristics through a greedy set coverage algorithm. The improved TSSM scheme provides reduced migration costs and lower controller resource consumption, as well as TSSM benefits. For its feasibility, the implementation of the proposed scheme is accomplished through an open network operating system. The experimental results show that the proposed improved TSSM scheme reduces the migration cost and lowers the controller resource consumption by about 36% and 34%, respectively, as compared with the conventional TSSM scheme.

A DDoS Vulnerability Analysis System against Distributed SDN Controllers in a Cloud Computing Environment

Software-Defined Networking (SDN) is now a well-established approach in 5G, Internet of Things (IoT) and Cloud Computing. The primary idea behind its immense popularity is the separation of its underlying intelligence from the data-carrying components like routers and switches. The intelligence of the SDN-based networks lies in the central point, popularly known as the SDN controller. It is the central control hub of the SDN-based network, which has full privileges and a global view over the entire network. Providing security to SDN controllers is one such important task. Whenever one wishes to implement SDN into their data center or network, they are required to provide the website to SDN controllers. Several attacks are becoming a hurdle in the exponential growth of SDN, and among all one such attack is a Distributed Denial of Service (DDoS) attack. In a couple of years, several new SDN controllers will be available. Among many, Open Networking Operating System (ONOS) and OpenDayLight (ODL) are two popular SDN controllers laying the foundation for many other controllers. These SDN controllers are now being used by numerous businesses, including Cisco, Juniper, IBM, Google, etc. In this paper, vulnerability analysis is carried out against DDoS attacks on the latest released versions of both ODL and ONOS SDN controllers in real-time cloud data centers. For this, we have considered distributed SDN controllers (located at different locations) on two different clouds (AWS and Azure). These controllers are connected through the Internet and work on different networks. DDoS attacks are bombarded on the distributed SDN controllers, and vulnerability is analyzed. It was observed with experimentation that, under five different scenarios (malicious traffic generated), ODL-3 node cluster controller had performed better than ONOS. In these five different scenarios, the amount of malicious traffic was incregradually increased. It also observed that, in terms of disk utilization, memory utilization, and CPU utilization, the ODL 3-node cluster was way ahead of the SDN controller.

Performance Evaluation of Stateful Firewall-Enabled SDN with Flow-Based Scheduling for Distributed Controllers

Software-defined networking (SDN) is a network approach achieved by decoupling of the control and data planes. The control plane is logically centralized and the data plane is distributed across the network elements. The real-time network is in need of the incorporation of distributed controllers to maintain distributed state information of the traffic flows. Software-based solutions aid distributed SDN controllers to handle fluctuating network traffic and the controller’s configurations are dynamically programmed in real time. In this study, SDN controllers were programmed with a stateful firewall application to provide firewall functionalities without the support of committed hardware. A stateful firewall filtered traffic based on the complete context of incoming packets; it continuously evaluated the entire context of traffic flows, looking for network entry rather than specific traffic flows. In addition, a flow-based scheduling module was implemented in the distributed controllers to improve network scalability. A network cluster was configured with three distributed controllers and we experimented with three independent network topologies. The performance of the proposed network model was evaluated by measuring and analyzing metrics such as network throughput (kbps), delay (ms) and network overhead (pkt/ms) for various combinations of controllers and topologies. The results of the analysis were determined using the mininet emulator. The findings of the performance evaluation indicate that the distributed SDN controllers performs better than a centralized controller. When comparing distributed SDN with two controllers and distributed SDN with three controllers the overall network throughput is increased by 64%, the delay is decreased by 43% and network overhead is reduced by 39%.

TSSM: Time-Sharing Switch Migration to Balance Loads of Distributed SDN Controllers

Software-defined networking (SDN) makes network management easier by using a controller to govern all switches, but the controller may become a performance bottleneck. Distributed SDN control is a promising solution, which lets multiple controllers divide the work, where each controller manages a part of the network. Switch migration is one common means to the load balance of controllers, which transfers some switches to different subnets based on the workloads of their controllers. The paper proposes a time-sharing switch migration (TSSM) scheme to provide more refined load sharing for controllers, which allows two controllers to share a switch’s load sequentially in the same period. When a controller is overloaded, TSSM finds assistant controllers to share its workload by selecting proper switches for migration and also deciding the time to perform migration. In this way, the workload of each controller can be kept below a given threshold. We implement the TSSM scheme on the open network operating system (ONOS) to attest to its feasibility. Experimental results show that TSSM can reduce 98% of the occurrences of overload for controllers as compared with the original OpenFlow method. Moreover, TSSM can save about 78% of the migration cost than the churn-triggered migration method.

Multiple-level threshold load balancing in distributed SDN controllers

The distributed Software-Defined Networking (SDN) approach has been adopted to address the scalability issue linked to the use of a single SDN controller, particularly in large-scale networks. However, this method gives rise to a new challenge of uneven load balance on the distributed SDN controller. Several studies tried to solve this issue but they have focused only on balancing the load when an overload occurs and have not achieved the load balance continuously. In this paper, we propose the multiple threshold load balance (MTLB) switch migration scheme to achieve continuous load balancing among the controllers. This approach classifies the load into several gradual levels that represents the basis for the switch migration when the load of a controller differs from others, which lead the threshold value to be dynamically adjusted. Moreover, our scheme reduces the overhead of unwanted update operations among the controllers by adopting the controller load status as an indicator for disseminating the load information. We implement our scheme using the Floodlight controller and Mininet emulator. The experimental results show that our scheme has better comprehensive performance than the other schemes in terms of response time, control overhead, and throughput rate.

Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers

Distributed controllers in software-defined networking (SDN) become a promising approach because of their scalable and reliable deployments in current SDN environments. Since the network traffic varies with time and space, a static mapping between switches and controllers causes uneven load distribution among controllers. Dynamic migration of switches methods can provide a balanced load distribution between SDN controllers. Recently, existing reinforcement learning (RL) methods for dynamic switch migration such as MARVEL are modeling the load balancing of each controller as linear optimization. Even if it is widely used for network flow modeling, this type of linear optimization is not well fitted to the real-world workload of SDN controllers because correlations between resource types are unexpectedly and continuously changed. Consequently, using the linear model for resource utilization makes it difficult to distinguish which resource types are currently overloaded. In addition, this yields a high time cost. In this paper, we propose a reinforcement learning-based switch and controller selection scheme for switch migration, switch-aware reinforcement learning load balancing (SAR-LB). SAR-LB uses the utilization ratio of various resource types in both controllers and switches as the inputs of the neural network. It also considers switches as RL agents to reduce the action space of learning, while it considers all cases of migrations. Our experimental results show that SAR-LB achieved better (close to the even) load distribution among SDN controllers because of the accurate decision-making of switch migration. The proposed scheme achieves better normalized standard deviation among distributed SDN controllers than existing schemes by up to 34%.

DSF: A Distributed SDN Control Plane Framework for the East/West Interface

The ever-increasing number of network-capable devices places a massive burden on modern networks. Communication infrastructure should provide quality-of-service essentials in terms of high-bandwidth capacity, scalability, resiliency, and security. Programmable networks are viewed as the prevailing method of encountering the challenges introduced by the accelerated expansion. The ability of software-defined networking (SDN) to separate the control plane from the data plane and enable the programmability of the network creates new ways to architect the network. The centralization of control logic introduces complexities in large-scale, distributed networks such as performance bottlenecks and reliability. Distributed SDN controllers have been proposed to overcome the performance concerns. The lack of a communication standard among distributed controllers, referred to as the East/West interface, presents a challenge in the adoption of SDN in large-scale, distributed networks. In this paper, we propose Distributed SDN control plane Framework (DSF) - a framework for the East/West interface for heterogeneous, distributed SDN controllers to synchronize topologies using a standardized data-centric real-time publish/subscribe paradigm known as the Data Distribution Service (DDS). Distributed control plane architectures are proposed using DSF: flat, hierarchical, and T-model. The DSF interface is implemented on multiple SDN control plane platforms to evaluate performance: Floodlight and Open Network Operating System (ONOS) controllers. Test cases with different configurations are designed for performance evaluation of the proposed interface in homogeneous and heterogeneous SDN control planes. In addition, a performance comparison is presented of DSF-based ONOS controllers versus Atomix-based ONOS cluster solutions.

Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT).

The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.

Fault Administration by Load Balancing in Distributed SDN Controller: A Review

Tremendous amount of data generated due to increasing no of users every day in the technology world. It is difficult to manage huge amount of discrete data with the traditional network. Even it is difficult to manage with technologies such as, big data, cloud computing in traditional network. Software Defined Networking (SDN) brings all the functionalities to a single location and making centralized decision. Controllers are the main entity of the SDN design, which governs decisions while routing packets. Centralized decision increases performance of the network. Distributed SDN controllers are physically distributed and logically centralized. Through this paper, we presented basics of distributed SDN controllers with its properties, studied classification of SDN controllers vide their logical design (hierarchical/flat model), programming language, adaptability, application domain etc. We categorize fault management issues in management plane, control plane and infrastructure plane and their interface. Paper will be concentrated on the faults generated through overloading of the controllers. As a solution switch migration technique will be derived. Provide detail discussion on prior work done in switch migration techniques for fault management through load balancing in distributed controllers. At last addition of fault management plane will be proposed in the SDN stack for generating robust distributed SDN controller.

Towards Minimum Inter-Controller Delay Time in Software Defined Networking

Abstract The fundamental idea behind Software Defined Networking (SDN) architectures is to decouple the control plane and data plane of the network to simplify the complexity of the network and centralize the control plane. A major feature of an SDN is the ability to have the controllers distributed over the network to solve the problem of a single point of failure at the controller, and to increase scalability and flexibility. Despite the flexibility and scalability of using multiple controllers, which facilitates the use of a larger number of network switches, yet the intercommunication cost between distributed controllers is still a key challenge in SDN. This paper presents an effective approach for the controller placement problem in software defined networks that aim to minimize the communication delay time between the controllers and the delay time between controller and switch. The major concept employed in this work is the Connected Dominating Set (CDS). Furthermore, an algorithm using the Minimum Connected Dominating Set (MCDS) is presented in which the delay time between the distributed SDN controllers is minimized. Simulation results are presented to show the efficiency of our approach compared to previous methods.

Enabling Digital Grid for Industrial Revolution: Self-Healing Cyber Resilient Platform

Driving digital grid development to provide sustainable, reliable and secure network systems requires deploying a variety of applications against any potential cyber attacks. Therefore, there is an urgent demand to accelerate the development of an intelligent Software-Defined Networking (SDN) platform that can address the tremendous challenges of data protection for digital resiliency. Modern grid technology tends to adopt distributed SDN controllers for further slicing power grid domain and protect the boundaries of electric data at network edges. To facilitate these techniques, this article proposes an intelligent secure SDN controller for supporting digital grid resiliency, considering management coordination capability, self-healing features and recovery of network traffic forwarding during service interruptions. A set of advanced features are employed in grid controllers to configure the network elements in response to possible disasters or link failures. In addition, various SDN topology scenarios are introduced for efficient coordination and configurations of network domains. Finally, to justify the potential advantages of an intelligent secure SDN system, a case study is presented to evaluate the requirements of secure digital modern grid networks and pave the path toward the next phase of industry revolution.

Low-Complexity Distributed SDN Controller Environments on Switch Migration Testbed

Control Path Management is an important component in the Distributed SDN Controller, and plays a key role in Software Defined Networking (SDN). Testbed simulations are done with an implementation of several controllers are deployed to improve the scalability and reliability of the control plane framework by employed network clustering into several subdomains with separate controllers. This paper investigates the deployment of distributed SDN controller but logically centralized systems for decouples control plane using Distributed Traffic Engineering mechanism. The results obtained show that the proposed mechanism can achieve controller load balancing with better performance.

Toward Adaptive and Scalable OpenFlow-SDN Flow Control: A Survey

Software-defined networking (SDN) is an emerging network architecture that promises to simplify network management, improve network resource utilization, and boost evolution and innovation in traditional networks. The SDN allows the abstraction and centralized management of the lower-level network functionalities by decoupling the network logic from the data forwarding devices into the logically centralized distributed controllers. However, this separation introduces new scalability and performance challenges in large-scale networks of dynamic traffic and topology conditions. Many research studies have represented that centralization and maintaining the global network visibility over the distributed SDN controller introduce scalability concern. This paper surveys the state-of-the-art proposed techniques toward minimizing the control to data planes communication overhead and controllers' consistency traffic to enhance the OpenFlow-SDN scalability in the context of logically centralized distributed SDN control plane architecture. The survey mainly focuses on four issues, including logically centralized visibility, link-state discovery, flow rules placement, and controllers' load balancing. In addition, this paper discusses each issue and presents an updated and detailed study of existing solutions and limitations in enhancing the OpenFlow-SDN scalability and performance. Moreover, it outlines the potential challenges that need to be addressed further in obtaining adaptive and scalable OpenFlow-SDN flow control.

ZeroSDN: A Highly Flexible and Modular Architecture for Full-Range Distribution of Event-Based Network Control

Recent years have seen an evolution of software-defined networking (SDN) control plane architectures, starting from simple monolithic controllers, over modular monolithic controllers, to distributed controllers. We observe, however, that today's distributed controllers still exhibit inflexibility with respect to the distribution of control logic. Therefore, we propose a novel architecture of a distributed SDN controller, providing maximum flexibility with respect to distribution and improved manageability. Our architecture splits control logic into lightweight control modules, called controllets, based on a micro-kernel approach, reducing common controllet functionality to a bare minimum and factoring out all higher-level functionality. Lightweight controllets also allow for pushing control logic onto switches and enable local processing of data plane events to minimize control latency and communication overhead while leveraging SDN's global view to maximize control decision quality. Controllets are interconnected through a message bus supporting the publish/subscribe communication paradigm with specific extensions for content-based message filtering. Publish/subscribe allows for complete decoupling of controllets to further facilitate control plane distribution. Furthermore, we identify crucial requirements for practical on-switch deployments, where we employ lightweight virtualization techniques to ensure a safe control plane operation. We evaluate both, the scalability and performance properties of our architecture, including its deployment on a white-box networking hardware switch.

Traffic pattern–based load‐balancing algorithm in software‐defined network using distributed controllers

SummaryProgrammability and decoupling of the data plane and control plane in software‐defined networking (SDN) make the enterprise's network to focus on this new paradigm and to deploy their applications on it. Furthermore, supporting of distributed controllers in SDN opens the opportunities to address the limitations of centralized controller's architecture, which in turn improves the overall performance of the network. This study proposes a new load‐balancing algorithm to handle the load based on the traffic pattern specifically transmission control protocol (TCP) and user datagram protocol (UDP) traffic. Additionally, this study uses a distributed SDN controller's architecture to host the load balancer application. This study also employs a failover mechanism on the distributed architecture to achieve high‐availability environment and to ensure the redundancy and reliability of the network. The obtained results prove the effectiveness of the proposed algorithm in terms of availability, which is increased by 11%, response time is reduced by 98%, transaction rate is also increased by 258%, throughput is increased by 206%, concurrency is reduced by 63%, and packet loss is reduced by 86% while comparing with random, round‐robin, and weighted round‐robin algorithms in addition to ease the integration and deployment in distributed controllers.

Response Time and Availability Study of RAFT Consensus in Distributed SDN Control Plane

Software defined networking (SDN) promises unprecedented flexibility and ease of network operations. While flexibility is an important factor when leveraging advantages of a new technology, critical infrastructure networks also have stringent requirements on network robustness and control plane delays. Robustness in the SDN control plane is realized by deploying multiple distributed controllers, formed into clusters for durability and fast-failover purposes. However, the effect of the controller clustering on the total system response time is not well investigated in current literature. Hence, in this work we provide a detailed analytical study of the distributed consensus algorithm RAFT, implemented in OpenDaylight and ONOS SDN controller platforms. In those controllers, RAFT implements the data-store replication, leader election after controller failures and controller state recovery on successful repairs. To evaluate its performance, we introduce a framework for numerical analysis of various SDN cluster organizations w.r.t. their response time and availability metrics. We use Stochastic Activity Networks for modeling the RAFT operations, failure injection and cluster recovery processes, and using real-world experiments, we collect the rate parameters to provide realistic inputs for a representative cluster recovery model. We also show how a fast rejuvenation mechanism for the treatment of failures induced by software errors can minimize the total response time experienced by the controller clients, while guaranteeing a higher system availability in the long-term.