Multiple Software Defined Networking Controllers Research Articles

Reducing CPU Utilization & Improving Failover Time in Dual Controller SDN (Software Defined Network) Environment

A salient component of the network is failover. Largely, customers are accessing dual path controller software defined network (SDN) environment. Subsequently, when one controller fizzles out the network communication can persist as a result of the other controller. There will be no interruption in the network communication due to the failover setup which will be effective in case of failure of a controller. In the absence of failover, the network communication will be obstructed. This could be by cause of the system resources, system itself or routing policies or Distributed Denial of Services. Generally, controller will support many switches. When SDN controller collapses controller will not be in sync with switches. After few attempts switches will failover to the secondary SDN controller. During this shutdown time the network communication will be obstructed. Failover algorithm is the key to solve this issue and will facilitate the synchronization of the information with SDN controllers and enhance failover time. Apparently, communication between both SDN controllers and switches is enhanced as well as reinforces the time of synchronization due to multiple SDN controllers. CPU utilization is another problem in SDN environment as the new inflow packets need to be forwarded to control plane to find the action that need to be taken. Processing at both ends and having one to one mapping between new incoming connection requests and the packet from data plane to control plane increases the CPU utilization and there by more power consumption.

A Novel Dynamic Software-Defined Networking Approach to Neutralize Traffic Burst

Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation.

Software-Defined Networking Orchestration for Interoperable Key Management of Quantum Key Distribution Networks.

This paper demonstrates the use of software-defined networking (SDN) orchestration to integrate regionally separated networks in which different network parts use incompatible key management systems (KMSs) managed by different SDN controllers to ensure end-to-end QKD service provisioning to deliver the QKD keys between geographically different QKD networks. The study focuses on scenarios in which different parts of the network are managed separately by different SDN controllers, requiring an SDN orchestrator to coordinate and manage these controllers. In practical network deployments, operators often utilize multiple vendors for their network equipment. This practice also enables the expansion of the QKD network's coverage by interconnecting various QKD networks equipped with devices from different vendors. However, as coordinating different parts of the QKD network is a complex task, this paper proposes the implementation of an SDN orchestrator which acts as a central entity to manage multiple SDN controllers, ensuring end-to-end QKD service provisioning to address this challenge. For instance, when there are multiple border nodes to interconnect different networks, the SDN orchestrator calculates the path in advance for the end-to-end delivery of keys between initiating and target applications belonging to different networks. This path selection requires the SDN orchestrator to gather information from each SDN controller managing the respective parts of the QKD network. This work shows the practical implementation of SDN orchestration for interoperable KMS in commercial QKD networks in South Korea. By employing an SDN orchestrator, it becomes possible to coordinate multiple SDN controllers and ensure the efficient and secure delivery of QKD keys between different QKD networks with varying vendor equipment.

Fault-Tolerant Controller Placement Model by Distributing Switch Load among Multiple Controllers in Software-Defined Network

One of the features of a software-defined network (SDN) is a logically centralized control plane hosting one or more SDN controllers. As SDN controller placement can impact network performance, it is widely studied as the controller placement problem (CPP). For a cost-effective network design, network providers need to minimize the number of SDN controllers used in the network since each SDN controller incurs installation and maintenance costs. Moreover, the network providers need to deal with the failure of SDN controllers. Existing studies that consider SDN controller failures use the scheme of connecting each SDN switch to one Master controller and one or more Slave controllers. The problem with this scheme is that the computing capacity of each SDN controller cannot be used efficiently since one SDN controller handles the load of all SDN switches connected to it. The number of SDN controllers required can be reduced by distributing the load of each SDN switch among multiple SDN controllers. This paper proposes a controller placement model that allows the distribution against SDN controller failures. The proposed model determines the ratios of computing capacity demanded by each SDN switch on the SDN controllers connected to it. The proposed model also determines the number and placement of SDN controllers and the assignment of each SDN switch to SDN controllers. Controller placement is determined so that a network provider can continue to manage all SDN switches if no more than a certain number of SDN controller failures occur. We develop two load distribution methods: split and even-split. We formulate the proposed model with each method as integer linear programming problems. Numerical results show that the proposed model reduces the number of SDN controllers compared to a benchmark model; the maximum reduction ratio is 38.8% when the system latency requirement between an SDN switch and an SDN controller is 100[ms], the computing capacity of each SDN controller is 6 × 106[packets/s], and the maximum number of SDN controllers that can fail at the same time is one.

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.

Collaborative detection and mitigation of DDoS in software-defined networks

This research presents the detection and mitigation of distributed denial of service (DDoS) in software defined networks (SDN). The proposed method consists of three modules: classifier module, mitigation module, and collaborative module. An ensemble classifier called V-NKDE is capable of detecting DDoS attacks accurately. The mitigation module blocks malicious traffics and purges entries of malicious traffic from the switch flow table. The collaborative module shares DDoS detection and mitigation rules among multiple SDN controllers using Redis Simple Message Queue mechanism. The proposed classifier performance validation on InSDN2020, CICIDS2017, NSL-KDD and UNSW-NB15 datasets. Furthermore we evaluated our proposed classifier in real traffic on an SDN simulation tested. The results show that the proposed method can detect DDoS attacks with high accuracy using an ensemble classifier, which performs better than single classifiers. More importantly, the false positive rate is greatly reduced, showing detection and mitigation of DDoS attacks across multi-controller domains with low controller overhead.

Near-Optimal Robust Virtual Controller Placement in 5G Software Defined Networks

Fifth generation (5G) wireless networksare characterized by applying the software defined networking (SDN) and network function virtualization (NFV) concepts to provide higher reliability and scalability, and lower latency for advent data-hungry services. In the initial version of SDN based 5G, a centralized architecture for SDN controller has been proposed, which inherently cannot fulfill those requirements simultaneously. This observation has led us toward distributed architectures, where multiple SDN controllers across the network constitute the control layers. Furthermore, to fully achieve the network flexibility and agility offered by SDN, and also improve resource utilization and consequently to reduce network cost, we incorporate virtualized SDN (vSDN) controllers, enabled through the NFV technology. In vSDN with distributed architecture, controller placement is one of the main challenges, which is an NP-hard combinatorial optimization problem with conflicting objectives: 1) maximizing the reliability of network with low latency; 2) minimizing the total cost of implementation, which can be interpreted as reducing the number of vSDN controllers. In order to achieve such a compromise, in this work, we formulate the problem of vSDN controller placement and introduce some performance metrics to model the latency of communication between controller-switch pairs and also robustness against vSDN controller failures in a network. The introduced formulation is some sorts of submodular optimization, which is exploited to develop several algorithms. Simulations results demonstrate that, with a negligible performance loss, a significant reduction in the number of vSDN controllers is achievable. Finally, latency, cost, and robustness trade-offs are investigated.

Load Balancing Algorithm for Migrating Switches in Software-defined Vehicular Networks

In Software-Defined Networks (SDN), the divergence of the control interface from the data plane provides a unique platform to develop a programmable and flexible network. A single controller, due to heavy load traffic triggered by different intelligent devices can not handle due to it’s restricted capability. To manage this, it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness. The flow of data through the multiple controllers also varies, resulting in an unequal distribution of load between different controllers. One major drawback of the multiple controllers is their constant configuration of the mapping of the switch-controller, quickly allowing unequal distribution of load between controllers. To overcome this drawback, Software-Defined Vehicular Networking (SDVN) has evolved as a configurable and scalable network, that has quickly achieved attraction in wireless communications from research groups, businesses, and industries administration. In this paper, we have proposed a load balancing algorithm based on latency for multiple SDN controllers. It acknowledges the evolving characteristics of real-time latency <i>vs.</i> controller loads. By choosing the required latency and resolving multiple overloads simultaneously, our proposed algorithm solves the load-balancing problems with multiple overloaded controllers in the SDN control plane. In addition to the migration, our algorithm has improved 25% latency as compared to the existing algorithms.

SYNCOP: An evolutionary multi-objective placement of SDN controllers for optimizing cost and network performance in WSNs

Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve better fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network’s state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously.In this paper, we first formulate the controller placement problem as a multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the Cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs.

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.

Collaborative Detection and Mitigation of Distributed Denial-of-Service Attacks on Software-Defined Network

This paper presents a collaborative technique to detect and mitigate Distributed Denial-of-Service (DDoS) flooding attacks on Software-Defined Network (SDN). This technique integrates sflow-RT application and Snort rules for the detection of DDoS traffic flows in an SDN controller. Redis Simple Message Queue (RSMQ) acts as a mechanism to share DDoS detection and mitigation rules among multiple Ryus SDN controllers. The rule-sharing allows a reduction of the controller’s overhead for processing DDoS detection and mitigation. The experimental results show that using the RSMQ mechanism can significantly detect and prevent DDoS attacks detection across multi-controller domains. It also provides early detection and mitigation of DDoS at lower controller overhead.

Blockchain-Enabled Software-Defined Industrial Internet of Things With Deep Reinforcement Learning

Recently, software-defined Industrial Internet of Things (SDIIoT), the integration of software-defined networking (SDN) and Industrial Internet of Things (IIoT), has emerged. It is perceived as an effective way to manage IIoT dynamically. Aiming to improve the scalability and flexibility of SDIIoT, multi-SDN has been applied to form a physically distributed control plane to handle a large amount of data generated by industrial devices. However, as the core of multi-SDN, reaching consensus among multiple SDN controllers is a thorny issue. To meet the required design principle, this article proposes a blockchain-enabled distributed SDIIoT to synchronize local views between distinct SDN controllers and finally reach the consensus of the global view. On the other hand, both the cryptographic operations of blockchain and the noncryptographic tasks have access to the same computational resource pool of mobile edge cloud (MEC). In order to optimize the system energy efficiency, we adaptively allocate computational resources and the batch size of the block by jointly considering the trust features of SDN controllers and the resource requirements of noncryptographic operations. To implement the truly distributed manner of blockchain, we describe our problem as a partially observable Markov decision process (POMDP) and propose a novel deep reinforcement learning (DRL) approach to solve it. In the simulation results, we compare three different protocols of blockchain and show the effectiveness of our scheme in each of them.

SDN-Based Internet of Autonomous Vehicles: An Energy-Efficient Approach for Controller Placement

The rapid advancement of the Internet of Things is expected to play a critical role in future intelligent transportation systems. This technology utilizes advanced information and communication technologies to enhance the operational capabilities of the vehicles and is often referred to as IoAV. More importantly, the increasing usage of sensors and other technologies generates vast amounts of data and information to be exchanged between AVs. Since the wireless connectivity between AVs is constantly expanding, the transmission of data is expected to pose several challenges to the conventional wireless networks, including resource utilization, network optimization, quality of service, and so on. To overcome these challenges, software-defined networking (SDN) has emerged as a powerful technology. This article presents a composite architecture named SD-IoAV for the integration of SDN with IoAV. This integration is not straightforward because SD-IoAV is geographically dispersed by nature. An efficient technique to manage the underlying communications is to deploy multiple SDN controllers across the widely dispersed SDN domains. This is referred to as the controller placement problem (CPP). Thus, the primary focus of this work is to explore CPP in the context of SD-IoAV as a special case of energy minimization and load balancing under latency restrictions. In this context, for large networks, the number of variables increases exponentially, which in turn escalates the complexity of the problem manifold. Hence, to deduce a near optimal solution for large networks, a heuristic approach based on the incremental expansion of candidate space is proposed. The simulation results have been carried out in MATLAB, and the obtained results show that the proposed scheme attains higher energy savings and better load capacity management compared to an existing technique, that is, improved performance by 18.73 and 9.42 percent, respectively.

A Policy-Based Security Architecture for Software-Defined Networks

As networks expand in size and complexity, they pose greater administrative and management challenges. Software-defined networks (SDNs) offer a promising approach to meeting some of these challenges. In this paper, we propose a policy-driven security architecture for securing end-to-end services across multiple SDN domains. We develop a language-based approach to design security policies that are relevant for securing SDN services and communications. We describe the policy language and its use in specifying security policies to control the flow of information in a multi-domain SDN. We demonstrate the specification of fine-grained security policies based on a variety of attributes, such as parameters associated with users and devices/switches, context information, such as location and routing information, and services accessed in SDN as well as security attributes associated with the switches and controllers in different domains. An important feature of our architecture is its ability to specify path- and flow-based security policies that are significant for securing end-to-end services in SDNs. We describe the design and the implementation of our proposed policy-based security architecture and demonstrate its use in scenarios involving both intra- and inter-domain communications with multiple SDN controllers. We analyze the performance characteristics of our architecture as well as discuss how our architecture is able to counteract various security attacks. The dynamic security policy-based approach and the distribution of corresponding security capabilities intelligently as a service layer that enables flow-based security enforcement and protection of multitude of network devices against attacks are important contributions of this paper.

SDQIM: Software-Defined Quadcopter Interdomain Management Protocol With Decentralized Inductive Power Transfer (DIPT) Platform

Due to the flexible deployment, low-cost and management, unmanned aerial vehicles, quadcopters have attracted significant interest lately in expanding wireless communications to areas that lack to cellular coverage network. Currently, software-defined network (SDN) is considered a viable alternative that improves network scalability and management. Earlier in 2018, we designed a novel software-defined quadcopter (SDQ) with a mobile Internet of Things vehicle to measure the best efficient wind power location for effective future wind turbine placement. However, large wind farms require complex and scalable wind measurement platform that consists of multiple quadcopters and ground mobile nodes that operate under a decision-making softwarized protocol. To this end, this paper proposes a novel cognitive routing protocol called SDQ interdomain management (SDQIM) Protocol. The SDQIM structure consists of multiple SDN controllers with failover capability. Our proposed protocol operate with multiple phases as each phase is used to cover a specific scenario that could occur in the interdomain network. The SDQ-main will have a total overview of the network topology and the SDQ-slave will be responsible for communication management with all other local subdomains. However, for large-scale farmland, quadcopter power level is essential for the continuity of the system services. Thus, we additionally propose a decentralized inductive power transfer (DIPT) platform that operates autonomously using software defined controlling approach. In essence, The DIPT platform is considered to be automated and low-cost approach that encompasses a custom-designed SDN controller for all interdomain quadcopters. Our system has been implemented by using an experimental testbed. The experimental results showed that scalability of the SDQ system can be increased to cover large wind farms with uninterrupted power management platform.

A Distributed Dynamic Adaptive and Fast Balancing SDN Controller Management

Tradition Software Defined Network (SDN) suffers from single point failure and uneven load distribution problems. Multiple SDN controllers work together to construct a distributed dynamic adaptive cluster with dynamic load balancing for large scale SDN switch stream requesting. Introducing such features as CPU, memory and network load, a Dynamic Adaptive and Fast algorithm (DAF) can select source, target and destination SDN controllers to balance load dynamically. The results of experiments show that DAF works well and improve the cloud service system throughput.

A Load-Balancing Mechanism for Distributed SDN Control Plane Using Response Time

Software-defined networking (SDN) has become a popular paradigm for managing large-scale networks including cloud servers and data centers because of its advantages of centralized management and programmability. The issues of scalability and reliability that a single centralized controller suffers makes distributed controller architectures emerge. One key limitation of distributed controllers is the statically configured switch-controller mapping, easily causing uneven load distribution among controllers. Previous works have proposed load-balancing methods with switch migration to address this issue. However, the higher-load controller is always directly considered as the overloaded controller that need to shift its load to other controllers, even if it has no response time delay. The pursuit of absolute load-balancing effect can also result in frequent network delays and service interruptions. Additionally, if there are several overloaded controllers, just one controller with the maximum load can be addressed within a single load-balancing operation, reducing load-balancing efficiency. To address these problems, we propose SMCLBRT, a load-balancing strategy of multiple SDN controllers based on response time, considering the changing features of real-time response times versus controller loads. By selecting the appropriate response time threshold and dealing with multiple overloading controllers simultaneously, it can well solve load-balancing problem in SDN control plane with multiple overloaded controllers. Simulation experiments exhibit the effectiveness of our scheme.

Distributed controller clustering in software defined networks.

Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

Flow Stealer: lightweight load balancing by stealing flows in distributed SDN controllers

Software-defined networking (SDN) introduces centralized controllers to increase network programmability drastically. Distributed control planes, in which multiple SDN controllers work together to manage a network, have been proposed to satisfy the requirements of large-scale networks, and different kinds of load-balancing approaches have been proposed to balance the workloads among these controllers. Current load-balancing approaches generally use switch migration, which adjusts the mapping between switches and controllers dynamically according to controller workloads. These switch migration-based approaches face challenges under burst traffic as a result of their overhead and longer detection periods. This paper proposes Flow Stealer, a lightweight load-balancing method for distributed SDN controllers. Flow Stealer uses a low-cost flow-stealing method, in which idle controllers share workloads temporarily with overloaded controllers by stealing flow events from them. The flow-stealing method not only can react to changes of network traffic more quickly, but can also reduce the frequency of switch migration. In addition, Flow Stealer incorporates both flow stealing and switch migration to adapt to burst traffic and long-term traffic changes. Experimental results show that Flow Stealer can balance the workloads among controllers more efficiently, especially under burst traffic.

New method for controller-to-controller communication in distributed SDN architecture

Software defined networking (SDN) is a promising technology that separates the control plane from the data plane in network devices and allows for programmability, flexibility, and efficiency in networks. Adopting this technology for data centres, cloud, and WAN networks could simplify network management. However, SDN was not initially designed for distributed architectures and could not handle a huge amount of data coming from a large network. We aim, in this paper, at providing new architecture composed of multiple SDN controllers, where each controller uses a dedicated interface to communicate with neighbouring controllers. Also, we provide several modes to customise this communication. Last, we show an initial proof-of-concept demonstration of our design.