A Systematic Review of Load Balancing Techniques in Software-Defined Networking

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.

Routing and load balancing are two important factors that guarantee Quality of Service (QoS) in the Internet. Meanwhile Software Defined Networking (SDN) has introduced a new networking paradigm that make routing and load balancing policies implemented more quickly and flexibly in the system. In the SDN architecture, the "intelligence" of the network is centralized in the application layer and control layer instead of distributing in the network device in traditional network. Current studies using SDN-based routing and load balancing still stop at the scope of finding appropriate network resource for one single data flow but not considering network resource for all flows coming from different sources as the whole. In this paper, we propose a global load-balanced routing scheme, which can take advantages of global view of the SDN controller to make a global policy for routing and load balancing. The solution has been shown to outperform traditional algorithms and recent SDN-based artificial intelligence algorithms in terms of delay and network utilization.

As the importance of communication networks increases in our lives, the limitations of traditional networks start to emerge. Software Defined Networking (SDN) is the most recent paradigm in the networking industry, its purpose being to mitigate traditional network limitations, such as complexity, the difficulty of introducing new services in the network, the inability of enforcing security policies while having a network-wide view. From a security point of view, the need for middleboxes in the network, such as firewalls or Intrusion Detection/Prevention Systems (IDS/IPS) is eliminated by implementing these functionalities in software applications. As SDN has the potential of becoming a key enabler for the Internet of Things (IoT), there are specific aspects of security for IoT that need to be taken into account, for example the lack of powerful computing resources or limited battery life, making securing IoT devices more challenging. This paper addresses one of these security issues, while evaluating a simple IPS application for an SDN controller. An emulated IoT network is controlled by the SDN controller, which also runs an IPS application. When a node becomes faulty or it is compromised and it sends too much traffic, that could cause a Denial of Service (DoS) in the network, it is blocked by the controller for a configurable amount of time.

The flexibility and programmability offered by Software Defined Networking (SDN) emerge as an approach to resolve some of the main challenges that the Internet of Things (IoT) deployments expose. In the present article, we explore the application of SDN components onto IoT deployments, together with the decoupling of typical gateway functions in two realms; the physical one and the virtual at the edge. This disengagement is a novel approach necessary since the computation resources on the physical part of the infrastructure are limited. Thus, the more resource-consuming processes are transferred to the edge of the network, in the virtual counterpart of the gateway. Additionally, within this infrastructure, different network applications can be running at the top of the SDN controller, in the control plane, for example, QoS policies application, management of network parameters, etc. Providing a holistic view of the IoT deployment. Between its advantages, this work focus on the ease of creation of an IoT infrastructure with the gateway and SDN architecture, and the behaviour of the IoT system. For validation, a small setup scenario and a use case is created where heterogeneous IoT sensors communicate with the physical part of the gateway, implemented with a Raspberry Pis and this, with its virtual part at the edge of the network, so finally sent the data to an IoT platform (FIWARE-ORION) at the core network managed by a virtual Switch (OpenVSwitch) and a RYU-based SDN controller.

The evolution and advancements in Information and Communication Technologies (ICT) have enabled large scale distributed computing with a huge chunk of applications for massive number of users. This has obviously generated large volumes of data, thus severely burdening the processing capacity of computers as well as the inflexible traditional networks. State-of-the-art methods for addressing datacenter level performance fixes are yet found wanting for sufficiently addressing this huge processing, storage, and network movement with proprietary protocols for this voluminous data. In this chapter the works have focused on addressing the backend server performance through effective reducer placement, intelligent compression policy, handling slower tasks and in-network performance boosting techniques through effective traffic engineering, traffic classification, topology discovery, energy minimization and load balancing in datacenter-oriented applications. Hadoop, the defacto standard in distributed big data storage and processing, has been designed to store data with its Hadoop Distributed File System (HDFS) and processing engine MapReduce large datasets reliably. However, the processing performance of Hadoop is critically dependent on the time taken to transfer data during the shuffle generated during MapReduce. Also, during concurrent execution of tasks, slower tasks need to be properly identified and efficiently handled to improve the completion time of jobs. To overcome these limitations, three contributions have been made; (i) Compression of generated map outputs at a suitable time when all the map tasks are yet to be completed to shift the load of network onto the CPU; (ii) Placing the reducer onto the nodes where the computation done is highest based on a couple of counters, one maintained at the rack level and another at node level, to minimize the run-time data copying; and (iii) Placing the slower map tasks onto the nodes where the computation done is highest and network is handled by prioritizing. Software defined networking (SDN) has been a boon for next generation networking owing to the separation of control plane from the data plane. It has the capability to address the network requirements in a timely manner by setting flows for every to and fro data movement and gathering large network statistics at the controller to make informed decisions about the network. A core issue in the network for the controller is traffic classification, which can substantially assist SDN controllers towards efficient routing and traffic engineering decisions. This chapter presents a traffic classification scheme utilizing three classifiers namely Feed-forward Neural Network (FFNN), Logistic Regression (LR), Naïve Bayes and employing Particle Swarm Optimization (PSO) for improved traffic classification with less overhead and without overlooking the key Quality of Service (QoS) criterion. Also lowering energy minimization and link utilization has been an important criterion for lowering the operating cost of the network and effectively utilizing the network. This issue has been addressed in the chapter by formulating a multi-objective problem while simultaneously addressing the QoS constraints by proposing a metaheuristic, since no polynomial solution exists and hence an evolutionary based metaheuristic (Clonal Selection) based energy optimization scheme, namely, Clonal Selection Based Energy Minimization (CSEM) has been devised. The obtained results show the efficacy of the proposed traffic classification scheme and CSEM based solution as compared with the state-of-the-art techniques. SDN has been a promising newer network paradigm but security issues and expensive capital procurement of SDN limit its full deployment hence moving to a hybrid SDN (h-SDN) deployment is only logical moving forward. The usage of both centralized and decentralized paradigms in h-SDN with intrinsic issues of interoperability poses challenges to key issues of topology gathering by the controller for proper allocation of network resources and traffic engineering for optimum network performance. State-of-the-art protocols for topology gathering, such as Link Layer Discovery Protocol (LLDP) and Broadcast Domain Discovery Protocol (BDDP) require a huge number of messages and such schemes only gather link information of SDN devices leaving out legacy switches’ (LS) links which results in sub-optimal performance. This chapter provides novel schemes which unearth topology discovery by requiring fewer messages and gathering link information of all the devices in both single and multi-controller environments (might be used when scalability issue is prevalent in h-SDN). Traffic engineering problems in h-SDN are addressed by proper placement of SDN nodes in h-SDN by utilizing the analyzing key criterion of traffic details and the degree of a node while lowering the link utilization in real-time topologies. The results of the proposed schemes for topology discovery and SDN node placement demonstrate the merits as compared with the state-of-the-art protocols.

The traditional load balancers suffer from inflexibility and difficulty to manage network flows and they are very expensive hardware dedicated devices. Software-Defined Networking (SDN) gives a promising solution for traditional load balancer limitations. SDN is a new paradigm which enables programmability and centralized control for the network which offers inexpensive and flexible solutions. This paper aims to develop a new load balancing approach using SDN. We propose a “Bandwidth-based” load balancing approach that distributes the network requests among multiple servers based on the servers' bandwidth consumption. The performance of the proposed scheme is evaluated and compared with different load balancing schemes (Round-robin and Connections-based) under a mininet emulation and a Raspberry Pi-based implementation. Emulation and hardware implementation results reveal a better performance of the proposed approach compared to the two reference schemes.

It is difficult to manage traditional networks due to network growth, an increase in user numbers, and the emergence of new technologies like big data and cloud computing. Consequently, it is required to alter the current network design. SDN is a well-liked architecture because it offers customizable control and centralized management in data centers. To achieve scalability and dependability, it was necessary to propose the geographical dispersal of a logically centralized control plane due to the huge scale of networks. Software-defined networking (SDN) load-balancing optimization has been studied for a long time. Many approaches to the load-balancing conundrum have been put forth by researchers, but very few have taken the impact of transmission delay between controllers and switches under heavy network load into account. In this article we elaborate the various techniques of load balancing in SDN those working on different areas like multi-controller, switch migration, multi-agent, strategy, time sharing, prediction, reinforcement learning etc. We discuss the methods used along with the advantages and disadvantages of various costs, service quality, and network performance metrics.

Considering the problem that the SDN controller can not fully resolve the virtual network function during the Software Defined Network (SDN) and Network Function Virtualization (NVF) collaborative deployment in the satellite network, a SDN/NFV coordination based on the three-layer satellite network is designed. The framework extends the SDN controller function and proposes the SDN controller function extension method (SDN-FE) based on LightGBM algorithm. SDN-FE has extended the functions of the original SDN controller in three aspects. One is based on LightGBM to establish a classification model for intelligent forwarding of SDN controller data packets. The second is to learn and predict VNF modification of data packets through logistic regression. The third is to design an idle state monitoring mechanism. The above method effectively enhances the control capability of the SDN controller to the network. In terms of SDN packet forwarding classification, SDN-FE is compared with existing well-known machine learning classification algorithms such as SVM, random forest, LSTM. Simulation experiments show that the SDN packet forwarding classification problem is predictable. The average accuracy of the LightGBM model in multi-classification problems is over 69%, which is better than machine learning models such as SVM and random forest. The next step is to conduct an in-depth study of the packet rule learning algorithm in VNF.

Software-defined networking (SDN) is an emerging network architecture that decouples the control plane from the data plane, enabling centralized programmability and management of networks. While SDN offers benefits like flexibility, scalability, and automation, it also introduces new security vulnerabilities. This literature review analyzes the current state of research on SDN security in three key domains – Internet of Things (IoT) environments, cloud computing, and traditional enterprise networks. A systematic review methodology was followed to search, select, and review 53 relevant studies published in the past 5 years. The analysis focuses on identifying common SDN threat vectors, security solutions proposed leveraging SDN programmability and evaluating their effectiveness based on results from simulations, testbed experiments, and initial real-world implementations. Key findings of the review include lack of authentication, susceptibility to DDoS attacks, and flow rule conflicts as major security issues in SDN across domains. Dynamic traffic monitoring, access control, policy orchestration, and virtualized security functions are commonly proposed techniques to enhance SDN security. However, limitations exist in robustness testing at scale, emerging paradigms like fog computing, and quantitatively comparing SDN security with legacy networks. As SDN adoption expands, focused efforts are needed to address these research gaps through innovations in data-driven security, coordinated security policy, and emphasizing SDN controller security. This review provides valuable insights into the current state of SDN security research and informs future efforts needed in this important area

Load balancing distributes incoming network traffic across multiple controllers that improve the availability of the internet for users. The load balancing is responsible to maintain the internet availability to users in 24 hours by 7 days a week. However, the internet become unavailable since the load balancer is inflexibility, costly, and non-programmable for settings adjustment especially in managing the network traffic congestion. An increasing user using mobile devices and cloud facilities, the current load balancer has limitations and demands for the deployment of a Software-Defined Network (SDN). SDN decouples network control, applications, network services, and forwarding roles; hence makes the network more flexible, affordable, and programmable. Furthermore, it has been found that SDN load balancing performs intelligent action, efficient and maintains better QoS (Quality of Service) performance. This study proposes the application of SDN-based Load Balancing since it provides pre-defined servers in the server-farm that receive the arrived Internet Protocol (IP) data packet from various clients in the same number of loads and process orders for each server. Experiments have been conducted using Mininet™ and based on several scenarios (Scenario A, Scenario B, and Scenario C) of network topologies. Parameters used to evaluate the load balancing in SDN are throughput, delay, and jitter. Findings indicated that scenario A gives a high throughput, scenario B and C produce a low jitter values and scenario C produces the lowest delay. The impact of SDN brings a multi-path adaptive direction in finding the best route for a better network performance.

On the use of the genetic programming for balanced load distribution in software-defined networks

There has been an increase in the usage of Internet of Things (IoT), which has recently become a rising area of interest as it is being extensively used for numerous applications and devices such as wireless sensors, medical devices, sensitive home sensors, and other related IoT devices. Due to the demand to rapidly release new IoT products in the market, security aspects are often overlooked as it takes time to investigate all the possible vulnerabilities. Since IoT devices are internet-based and include sensitive and confidential information, security concerns have been raised and several researchers are exploring methods to improve the security among these types of devices. Software defined networking (SDN) is a promising computer network technology which introduces a central program named ‘SDN Controller’ that allows overall control of the network. Hence, using SDN is an obvious solution to improve IoT networking performance and overcome shortcomings that currently exist. In this paper, we (i) present a system model to effectively use SDN with IoT networks; (ii) present a solution for mitigating man-in-the-middle attacks against IoT that can only use HTTP, which is a critical attack that is hard to defend; and (iii) implement the proposed system model using Raspberry Pi, Kodi Media Center, and Openflow Protocol. Our system implementation and evaluations show that the proposed technique is more resilient to cyber-attacks.

Software Defined Networking (SDN) is widely used in many practical contexts and provides a method for managing networks by separating the data plane from the control plane. However, because of its centralized control logic, SDN is particularly susceptible to Distributed Denial of Service (DDoS) attacks. In numerous studies, methods based on Machine Learning (ML) have been proposed to identify DDoS attacks in the context of SDN. However, the proposed methods are resource intensive and unreliable in a large-scale SDN where a huge amount of traffic data is produced from control and data planes. This may exhaust computational resources, deteriorate the performance of the network, or even result in the shutdown of network systems as a result of resource exhaustion. To deal with the above issues, this paper presents a real-time DDoS detection and mitigation method in a multi-controller SDN environment using cloud computing. We propose to run the Machine Learning development cycle on a secured server in a virtual machine. Then we deploy back the trained model into the SDN controllers. All the exchanges between the virtual machine and the controllers are secured. In this way, we configure these controllers to detect and mitigate DDoS in SDN in real-time. Through useful experiments, we demonstrate that our method is secure, reliable, and efficient.

Software Defined Network (SDN) Technology made tremendous changes in networking field by controlling the hardware with remote software by decoupling data plane and control plane. The focus here is on the controller which is part of control plane. A controller is a central part of SDN; it is an intermediary between Data plane and Forwarding plane. It takes prime place in SDN architecture like Operating System in Computers. SDN Controller is a software which directs the packets using flow rules and also contains statistical information of network. The controller orchestrates the networking devices such as switches and routers by channelizing the packets. SDN controller has different flavor based on the commercial requirement achieved by including new pluggable module. This paper discusses some of the SDN Controller with their feature; the controllers are POX, NOX, Floodlight, ODL, Open Contrail. This paper also compares the characteristic of centralized and distributed controller by focusing on performance, bandwidth and round-trip time of Floodlight and Open Daylight (ODL) controller. The simulation is conducted using Mininet.

Software Defined Networks (SDN) have been utilized in applications of the Internet of Things (IoT), termed as software-defined IoT network, because of the popularity and capability of mobile devices being used for networking in relatively large areas. In a software-defined IoT system, the sensing data are asynchronously harvested by the mobile sensing nodes, and are also asynchronously uploaded to the gateways of a software-defined network. Thus, all the sensing data are asynchronously transmitted from the gateways to the data servers in the pattern of multipoints-to-point (M2P) data transmissions. Even if the sensing data are generated from the same sensing event, the controller of SDN has no knowledge about this relationship. Thus, such asynchronous M2P data transmissions from the same sensing event at the gateways will generate many redundant requests to their controller by OpenFlow protocol of SDN. In this paper, we investigate the redundant requests caused by the asynchronous M2P data transmissions in the software-defined IoT network. We model the relationship between the sensing events and the uploading gateways by utilizing their spatial locations and the distribution of mobile sensor nodes. To reduce the loads on the controller for the asynchronous M2P data transmissions, we propose an One-Request Scheme for Software-Defined IoT Networks (ORSIN), to batch the updating the forwarding rules of the multiple data transmissions from the same event by the first one request from a gateway. Our extensional simulations verify the effectiveness of our proposed approach.