Software defined networking: Viewpoint from IP networking, PROS nd CONS and exploration thoughts

This article addresses the joint switch and controller placement problem in a hybrid software-defined network (SDN), which is an intermediate step of transforming a traditional backbone network into a pure SDN. For a hybrid SDN, the quality of service (QoS) is a primary concern for ensuring service guarantee of a traditional network, while providing additional benefits of softwarization. In the SDN, the positions of SDN controllers determine the QoS parameters, such as network throughput and flow processing delays. Many existing solutions determine the controller locations to optimize the latency of network links and control plane load. However, there is a need to address the problem considering additional aspects of the hybrid SDN, such as SDN switch placement and incremental upgrades. In this article, we propose a priority-based SDN switch placement approach and an incremental controller placement approach for the hybrid SDN. Simulation results depict that the proposed scheme increases programmable traffic by 9.90% compared to the existing MapFirst scheme when the switch upgrade budget is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> 400 K. The proposed scheme decreases QoS violation by 20.89% compared to the Greedy approach with 0.3 million flows.

Quality of Service on TCP / IP networks is focused on allocating bandwidth that flows in accordance with service requirements on the network. It aims to allocate guaranteed bandwidth for each service used. The increasing number of Internet users on campus networks will automatically affect the quality of services on the network. Therefore, we need a system that can improve the quality of network services by limiting the bandwidth for each service that runs. Hierarchical Token Bucket (HTB) is one method of bandwidth allocation. HTB has the task of limiting bandwidth and arranging packet queues based on priority. HTB method application is done using Software-Defined Networking (SDN) technology. SDN is a network technology that uses OpenFlow as the protocol. While OpenDaylight is one of the SDN-based controllers that supports the OpenFlow protocol. The HTB method is connected to the OpenDaylight controller via the REST API. The results obtained from four types of scenarios performed, resulting in a throughput of 10.15 Mbps, 7.08 Mbps, 3.08 Mbps for HTTP, FTP, and SMTP services respectively.

It is Software defined networking (SDN) is a new network approach which allows separation of the forwarding plane from the control plane in network architecture, that aids in the efficient control of data flow and gives network administrators a software- based method for managing and configuring the networks. In contrast to SDN, which provides a centralized controller, traditional network topologies require the individual configuration of each network device. Due to a centralized control functionality, SDN is increasingly used by businesses, telecom service providers, cloud providers, and many other users, as it encourages flexibility and scalability in the network while also assisting in streamlining operations. There is significant demand of SDN for effective and adaptable networks. SDN adoption has also been rising in the campuses and corporate sectors. This paper provides a concise overview of the Software Defined Networks (SDN) topic, which is essential for comprehending the fundamental principles of SDN in order to conduct research and implement SDN in many sectors. The initial focus is on the discussion of traditional networks, including their limitations, as well as the many issues encountered by industries. The topic of Software Defined Networks is explored, encompassing their necessity, historical background, architectural structure, interfaces, and models. Next, the many applications of Software-Defined Networking (SDN) are examined. Additionally, the various SDN case studies, vendors, setup issues with SDN, and current research fields were elucidated. The discussion focuses on the role of Software-Defined Networking (SDN) in Artificial Intelligence (AI), specifically addressing the issues associated with implementing SDN in AI.

The issue of supporting Quality of Service (QoS) for media applications and other different applications has increased tremendously in industry and the scholarly community. The traditional network architecture became overloaded and congested due to the continued demand for network resources and the limited bandwidth available. The user experience and the academic studies have indicated an increase in limitations and weaknesses of these architectures. A new network architecture paradigm, called Software-Defined Networks (SDN), has received massive attention from researchers and it is used to find new solutions to the limitations found in traditional network architectures. This survey presents an overview of QoS in a traditional network, as well as in SDN. The existing SDN platforms, controllers, and protocols are presented as well as the multimedia flow routing mechanisms being investigated. The survey outcomes will be useful for people in the industry as well as academic researchers identifying current limitations and challenges of QoS within SDN and traditional network architectures.

The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.

The Internet has caused the advent of a digital society; wherein almost everything is connected and available from any place. Thus, regardless of their extensive adoption, traditional IP networks are yet complicated and arduous to operate. Therefore, there is difficulty in configuring the network in line with the predefined procedures and responding to the load modifications and faults through network reconfiguring. The current networks are likewise vertically incorporated to make matters far more complicated: the control and data planes are bundled collectively. Software-Defined Networking (SDN) is an emerging concept which aims to change this situation by breaking vertical incorporation, promoting the logical centralization of the network control, separating the network control logic from the basic switches and routers, and enabling the network programming. The segregation of concerns identified between the policies concept of network, their implementation in hardware switching and data forwarding is essential to the flexibility required: SDN makes it less complicated and facilitates to make and introduce new concepts in networking through breaking the issue of the network control into tractable parts, simplifies the network management and facilitate the development of the network. In this paper, the SDN is reviewed; it introduces SDN, explaining its core concepts, how it varies from traditional networking, and its architecture principles. Furthermore, we presented the crucial advantages and challenges of SDN, focusing on scalability, security, flexibility, and performance. Finally, a brief conclusion of SDN is revised.

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 new efficiently idea of programmable networks that separates the control plane from data plane of all network devices. Internet service provider is responsible for all the control decisions and communication among the forwarding elements from centralized controller. SDN provides the various optimized services. Quality of service (QoS) routing is a path computation method that is suitable for the different traffics generated by several applications, while utilization of network resources has increased. This agreement of service is defined by QoS requirements such as throughput, delay, jitter and packet loss etc. Multimedia applications often require assured from multi QoS constrained, causing the NP-complete problem which cannot be simply solved in polynomial time and high management complexity in the transition network. SDN is able to reduce complexity and it is used to efficiently implement traffic, hence SDN significantly values to development QoS routing. In this paper, we propose QoS routing algorithm called Quantized Level Balance (QLB) for SDN that considers one or many QoS parameters relating to the network application. To satisfy the requirements, QLB selects QoS parameters depending to the level of appropriate application service quality. We have replicated our algorithm on simulate topology with Scalable Video-streaming Evaluation Framework (SVEF). We measure the Peak Signal-to-Noise Ratio (PSNR) and Mean Opinion Score (MOS) of Scalable Video Coding (SVC) at the receiver. Our propose algorithm is improved than single-metric approach that may choose poor QoS parameter paths.

The traditional networks are facing difficulties in managing the services offered by cloud computing, big data, and the Internet of Things as the users have become more dependent on their services. Software-Defined Networking (SDN) has pulled enthusiasm in the integration process of technologies and function as per the user's requirements for both academia and industry, and it has begun to be embraced in actual framework usage. The emergence of SDN has given another idea to empower the focal programmability of the system. Because of the increasing demand and the scarcity of resources, the load balancing issue needs to be addressed efficiently to manage the incoming traffic and resources and to improve network performance. One of the most critical issues is the role of the controller in SDN to balance the load for having a better Quality of Service (QoS). Though there are few survey articles written on load balancing, there is no detail and systematic review conducted in load balancing in SDN. Hence, this paper extends and reviews the discussion with a taxonomy of current emerging load balancing techniques in SDN systematically by categorizing the techniques as conventional and artificial intelligence-based techniques to improve the service quality. The review also includes the study of metrics and parameters which have been used to measure the performance. This review would allow gaining more information on load balancing approaches in SDN and enables the researchers to fill the current research gaps.

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.

In this era wireless technology gaining more popularity, people prefer wireless communication over wired communication due to mobility, scalability and ease of access. However, the traditional wireless network is complex, vendor dependent and inadequate feasibility to incorporate new services, protocols, and APIs. The rapid growth in wireless network raises many thoughtful challenges for network and telecom operators. On such grounds, Innovation of Software Defined Network (SDN) and network function virtualization (NFV) for mobile network opens a new arena of research. This research article presents future mobile Internet architecture (FMIA) with the integration of twin technologies, SDN and NFV that enable rapid access to Internet services. Moreover, the network manager can control services provided by ISPs through proposed architecture.

The increase in size and complexity of the Internet has led to the introduction of Software Defined Networking (SDN). SDN is a new networking paradigm that breaks the limitations of traditional IP networks and upgrades the current network infrastructures. However, like traditional IP networks, network failures may also occur in SDN. Multiple research studies have discussed this problem by using a variety of techniques. Among them is the use of the community detection method is one of the failure recovery technique for SDN. However, this technique have not considered the specific problem of multiple link multi-community failure and inter-community link failure scenarios. This paper presents a community detection-based routing algorithm (CDRA) for link failure recovery in SDN. The proposed CDRA scheme is efficient to deal with single link intra-community failure scenarios and multiple link multi-community failure scenarios and is also able to handle the inter-community link failure scenarios in SDN. Extensive simulations are performed to evaluate the performance of the proposed CDRA scheme. The simulation results depicts that the proposed CDRA scheme have better simulations results and reduce average round trip time by 35.73%, avg data packet loss by 1.26% and average end to end delay 49.3% than the Dijkstra based general recovery algorithm and also can be used on a large scale network platform.

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.

Accurate traffic and latency measurements are the foundation of many network management tasks, including traffic engineering and network provision, network diagnosis and fault recovery. However, SNMP-based traffic measurement techniques in traditional IP networks only provide measurements for link loads, which cannot lead to accurate measurements. For link latency measurement, it has been shown that a traditional IP network needs to deploy a large number of monitors to achieve accurate measurements. The software-defined network (SDN) architecture opens up new opportunities to tackle the traffic matrix estimation and latency measurement problems. For the traffic estimation problem, the counter in a flow table entry of an SDN switch can directly measure the size of a matched flow. This dramatically increases the number and type of flows that can be measured accurately. For link latency measurement, SDN switches provide flexible routing and hence allow even non-shortest paths to be measured in the network. This routing flexibility can reduce the number of monitors deployed in the network to achieve a complete measurement of all the link latencies. But the additional measurement capabilities of SDNs also have limitations: the flow table entries added for measurements must be limited due to the high cost of ternary content-addressable memory (TCAM), and the amount of additional traffic for measurement should also be confined. Under these constraints, this research investigates the traffic matrix estimation and link latency measurement problems for a pure SDN network and a hybrid SDN network. A hybrid network, which consists of SDN switches and conventional routers, will exist for some time to come since it is economically impossible to replace every conventional router overnight. For the traffic matrix estimation problem, we propose a new estimation framework, which can be used in both SDNs and hybrid SDNs. Performance evaluation results show that the new framework can achieve highly accurate traffic measurement with a small number of TCAM entries. For the link latency measurement problem, we first present minimum monitor placement schemes in hybrid SDNs. It has been shown that our schemes not only reduce the number of required monitors but also guarantee full link measurements even when link weights change. We then present two time-stamp-based link latency measurement schemes for pure SDNs and show that the proposed schemes can minimize the number of consumed TCAM entries and the amount of traffic generated for measurement.

The proliferation of mobile demands and increasingly multifarious services pose challenges to both the radio access networks (RANs) and the core network (CN). Unfortunately, the traditional networks can hardly meet the increasing requirements. Software-defined network (SDN) and network virtualization (NV) are considered as innovative paradigms to stress these problems. By extending SDN to wireless networks, software-defined wireless network (SDWN) consisting of both RANs and CN can be obtained. In this paper, the RANs and CN of SDWN are virtualized to virtual access points (VAPs) and network slices respectively based on NV technology. Considering the system scenario that a SDWN consists of overlapping RANs, an optimal joint VAP and network slice selection scheme is proposed. To characterize the complicated transmission performance of VAPs and network slices, the concepts of utility function and network calculus are introduced, and the joint utility of VAPs and network slices is formulated based on which the optimal VAP and network slice combination corresponding to the maximal joint utility can be selected. The simulation results demonstrate the efficiency of the proposed scheme.