Optimizing Network Performance Using Software – Defined Networking (SDN)

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.

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

Artificial intelligence based load balancing in SDN: A comprehensive survey

Software Defined Networking (SDN) has a potential to address a number of networking challenges, but still presents other financial and business challenges such as budget constraints, acceptability and trust amongst network operators. A hybrid SDN deployment is a conceivable solution, because it offers the benefits of SDN and traditional network capabilities, including budget constraints adaptability, falling back on time-tested traditional tools, central network programmability and will offer an organization a smooth transition to SDN. As modern-day communication has become an imperative feature of day to day business functions, there is a need to balance the network load to achieve improved network performance, minimizing delay and reduce the time taken for network response. In this paper, we develop and study multi-path load balancing in a hybrid SDN data plane. The load balancer’s performance is tested using four different hybrid SDN topologies, namely, Fat Tree, Ring, Mesh and Torus topology with the OpenDayLight controller. The main contribution of this work is a data plane congestion and link overloading load balancing testing and analysis for deployment (before or after network failure) in hybrid SDN environments. The obtained results display an improved overall network performance and reduced network delay.

<span>The requirements <span>for the network are increasing by the expanding and spreading the Internet. The Previous techniques of the network do not meet the modern needs, thus, a new technical presents software-defined networking (SDN). SDN recognizes as a promising new model that separates the control plane (traffic routing and network topology) from the data plane (network architecture layers). The architecture of SDN has some features that find in a single controller or many controllers instance of programmable, flexible, and scalable. In the current SDN, multiple controllers are essential. Therefore, the optimal number of the controllers and their locations is the most significant challenge, known as the controller placement problem (CPP). It deploys the optimal number of controllers within the network while meeting presentations requirements considered conflicting in nature example: credibility, load balancing, latency, energy efficiency, and computation time. Many studies researched the ways to develop solutions for improving scalability, place selection for SDN. This paper presents the CPP and gives a comprehensive review of SDN issues based on the recent well-known research to extract available solution strategies. Finally, it discusses the limitations and future study directions that can support researchers in this field.</span></span>

Software defined network (SDN) provides flexible and scalable routing by separating control plane and data plane. With centralized control, SDN has been widely used in traffic engineering, link failure recovery, and load balancing. This work considers the flow update problem, where a set of flows need to be migrated or rearranged due to change of network status. During flow update, efficiency and consistency are two main challenges. Efficiency refers to how fast these updates are completed, while consistency refers to prevention of blackholes , loops , and network congestions during updates. This paper proposes a scheme that maintains all these properties. It works in four phases. The first phase partitions flows into shorter routing segments to increase update parallelism. The second phase generates a global dependency graph of these segments to be updated. The third phase conducts actual updates and then adjusts dependency graphs accordingly. The last phase deals with deadlocks, if any, and then loops back to phase three if necessary. Through simulations, we validate that our scheme not only ensures freedom of blackholes, loops, congestions, and deadlocks during flow updates, but is also faster than existing schemes.

Efficient network monitoring is an important basis work for network management. Generally, many management applications require accurate and timely statistics about network states at different aggregation levels at low cost, such as malicious traffic detection, traffic engineering, etc. Moreover, the network environment to be monitored is constantly changing and expanding, including not only the data center for cloud computing but also the Internet of Things (IoT) for smart urban sensing, which requires the intensive study of more fine-grained network monitoring. As is well known, the development of efficient network monitoring approaches greatly relies on a flexible monitoring framework. Software defined network (SDN) can provide dramatic advantages for network management by separating the control plane and data plane. Therefore, it is a good choice to design a flexible monitoring framework based on the advantages of SDN. However, most research works only take advantage of the centralized control feature in SDN, which leads to limited improvement in the flexibility of the monitoring framework. This paper proposes a flexible monitoring framework named FlexMonitor, which can realize greater flexibility based on not only the centralized control feature, but also the high programmability in the controller and the limited programmability in the openflow switches in SDN. There are two key parts in FlexMonitor, namely the monitoring strategy deployment part and the monitoring data collection part, which can enrich the deployment methods of monitoring strategies and increase the kinds of monitoring data sources, respectively. Based on the NetMagic platform, this monitoring framework was implemented and evaluated through realizing a distributed denial of service (DDoS) detection approach. The experimental results show that the proposed DDoS detection approach has a better detection performance compared with other related approaches as well as indirectly show that FlexMonitor can flexibly support a variety of efficient monitoring approaches.

This paper reviews the research on Traffic Engineering (TE) based on Software-Defined Networking (SDN). Firstly, the shortcomings of traditional network architectures in managing complex traffic are introduced, and it is pointed out that SDN provides a new solution for traffic engineering through the features of separation of control and data planes, centralized control, and programmability, but at the same time, it faces many challenges. Then the three-layer architecture of SDN and its role in traffic engineering are described, and the goals, research directions and application scenarios of traffic engineering are outlined. The application of machine learning (ML) methods in SDN traffic engineering is then discussed in detail, including the advantages of deep learning (DL) in traffic prediction and classification, and the potential of reinforcement learning (RL) in routing optimization and resource scheduling. In addition, four mechanisms for optimization algorithms to merge with ML are analyzed. The integration of SDN with new architectures such as edge computing and network slicing, as well as the prospects for the application of intent-driven networking are then explored. Finally, the opportunities and challenges of SDN traffic engineering are summarized, including the advantages of flexible traffic management and cross-domain optimization, as well as the problems of network scale, security, real-time and multi-tenant management, which provide directions for future research.

This paper is based on the fair analysis on the economic benefits of software defined networking. The paper will explain vividly the concept of software defined networking and its applications together with the economic advantage(s) with respect to both the client perspective and server perspective. In general, SDN provides lower hardware and operational cost in an excessive traffic load of nowadays users. It has tighter security and faster response time with improved management and planning of server controller. SDN when deployed provides better service(s) with respect to lowering cost and of course with better efficiency. Software Defined Network (SDN) is a great revolutionary idea to change IT Industry of which includes cloud computing and many others. The beauty of its architecture is the decoupling of the essential control and data planes from the application layer, thus the underlying infrastructure is abstracted but the network intelligence is logically centralized with state. This architecture gives access to the enterprise to control, program, automate, scale and secure the network according to the business needs, together with securing a safe benefit in competiveness that has risen due to fast advancement in technology. This architecture is even more important to fulfilling the demands of various clients in the current model of networking. The concept of SDN has the potential to change the current networking model. With SDN, for network services, the underlying network infrastructure can be abstracted by the administrators for many applications. In theory, it can be shown that SDN has many benefits over current IT systems but in practical systems, IT companies are reluctant to work with, tend to oppose nor are they ready to deploy SDN. In this paper, the economic benefits are explored and discussed without bias, and it will be shown that new protocol of SDN provides many economic benefits and guidance to IT companies to establish more secure and efficient network.

Software-Defined Networking (SDN) represents an innovative paradigm in network design that improves manageability, scalability, and adaptability. Within SDN, load balancing serves as a key component in optimizing network performance. SDN relying on a single centralized controller can lead to performance issues, especially under high traffic or excessive requests. To address this challenge, this study introduces an improved SDN load balancer that uses a Dynamic Weighted Round Robin algorithm with Distributed Controller (DWRR-DC), implemented using the Ryu controller. The proposed algorithm was implemented along with some existing load balancing methods, such as Random Selection (RS), Round Robin (RR), Least Time Weighted Round Robin (LTWRR), Weighted Round Robin (WRR), and Dynamic Weighted Random Selection (DWRS), emulated using the Mininet platform with a huge volume of simulated network traffic. Their performance was analyzed with respect to throughput and packet loss percentage, and the results were compared. The response time of the proposed and existing algorithms was also compared using the generated HTTP requests. Finally, the optimal number of controllers for the proposed algorithm was also obtained.

In traditional networking devices, there is a tight coupling of data plane and control plane. It handles a huge volume of traffic on the Internet which is not possible in traditional networking devices. The strategy emerged of disassociating the control plane from the data plane is defined as software-defined networking (SDN). The rapid increase in the number of Internet users is becoming a difficult task for conventional load balancers to handle network traffic. SDN provides power over the network from the centralized controller to distinguish between the control plane and the data plane. SDN is a prominent technology in today’s Internet. Nowadays, our networks handle the extensive amount of traffic that serves the hundreds and thousands of clients where it is very difficult for the single server to handle such a huge load. To overcome this problem, the load balancing technique came into existence. The main purpose of this work is to show how SDN applications can be created without using any controller and analyze the efficiency/effectiveness of load balancing algorithms based on SDN.

Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.

Driven by the requirement of increasing performance and flexibility, networks are being softwarized by paradigms such as software-defined networking (SDN) and network function virtualization (NFV). These solutions reduce the complexity and the specialization of hardware devices, by extracting the inherently distributed control plane of forwarding network elements such as switches and routers, to a logically centralized control plane (referred as controller in SDN). The control plane acts as a broker between the network applications (e.g. monitoring, traffic engineering) and the data plane (i.e. physical network infrastructure). For scalability and robustness, the logically centralized control plane is implemented by physically distributing different controllers throughout the network. This chapter presents different solutions to increase the reliability of both planes: data and control planes. The reliability of the data plane can be increased by considering survivable virtual network embedding solutions. This chapter proposes a survivable embedding against single and double failures at either links or nodes. Furthermore, in order to provide a programmable and resilient data plane, BPFabric has been proposed for SDN which supports high performance functions suitable for detecting attacks. On the other hand, the reliability of the control plane applied to SDN can be addressed by considering enhanced controller placement solutions providing redundancy against uncorrelated as well as targeted failures while coping with latency and capacity requirements. Furthermore, a solution to increase the security and robustness of the control channel is also addressed in this chapter.

In recent years, SDN has evolved as a promising alternative to the traditional networking paradigm. This is because the traditional network is decentralized, making it difficult to manage and provide trouble-shooting. SDN is an approach to network management whereby the forwarding process of the data plane (DP) is disassociated from the control plane (CP). The CP is responsible for decision-making. SDN centrally control the network infrastructure and make it programmable. Communication between the two planes (data and control) is carried out by the OpenFlow (OF) protocol. The deployment of SDN was initiated by Mr. Matrin Casado and his research team in 2008. The original projects developed at Stanford University were SANE and ETHANE. Security reports and web articles suggest that SDN (Software Defined Network) has the caliber to defend DDoS attacks. The year 2017 has been declared as the year of SDN adaption and DDoS mitigation. The initial effort in the direction of compiling the detection and mitigation approaches has been done in (Dayal et al., 2016). Recently a similar effort has been done in (Rochak et al., 2019). The CP of SDN has a complete visibility of the network which makes traffic monitoring feasible. Moreover, SDN open flow switches contain forwarding logic only; decision-making capability is softwarized at 58controller. This helps the controller to instruct and configure the switches with new flow rules which the switches follow. This facilitates the network admins to detect and mitigate these attacks easily. SDN have gained momentum among the possible solutions of DDoS attacks due to its decentralized nature of network management, but solutions come with its repercussions. The centralization and open architecture framework makes SDN difficult for widespread adaptation. The chapter begins with an introduction to DDoS attacks and its classification such as volumetric attack, protocol exploitation attack and application attack. In the next section, we discuss the possible DDoS attacks over SDN framework. We have categorized the vulnerability into three classes: DDoS attack on data, control, and application planes (APs). The DDoS attack on DP can further be classified into TCAM exhaustion, switch DDoS, ICMP flood, TCP flood, TCP_SYN flood, etc. We would discuss Resource depletion, OF bandwidth exhaustion, amplification attacks in DDoS attack over CP. HTTP flooding and Slowloris attack has been discussed in DDoS attack over AP. The next section highlights the working of attack tools used in performing DDoS attacks. Various DDoS tools have been widely exploited to render DDoS attacks and facilitate attackers to carry out dangerous attack on target; here target may be network resources, server resources, or applications. The fourth section focuses on the state-of-the-art for detection and mitigation approaches of DDoS attacks. Detection approaches are classified into statistical, policy, and machine learning-based approaches. DDoS mitigation OF switches, including Alcatel Lucent’s OmniSwitch, Brocade’s MLXe series routers, and applications such as DefensePro by Radware that are available in market; also depend on the statistical threshold to detect the DDoS attack. In addition, this section also incorporates a brief analysis of the previous defense mechanism of the last 10 years and analyzes and compares the products of different security vendors like Cloudflare, Radware, Akamai, Arbor, Nexusguard, etc.

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.