Robust multipath multicast routing algorithms for videos in Software-Defined Networks

Current infrastructures are reaching the point where existing networking methods are unable to cope with the exponential growth of traffic and Quality of Service (QoS) requirements. New techniques are necessary to keep pace. One such technique, Software-Defined Networking (SDN) uses a central controller to program many individual network devices. However, SDN uses heuristic algorithms that do not always select the optimal path. This paper looked at creating three Q-Routing algorithms leveraging SDN and Mesh network topologies. Two algorithms used one network metric each (Latency and Bandwidth) and the third used multiple metrics. Results showed that the single metric Q-Routing algorithms on average performed as well as the K-Shortest Path versions while Q-Routing with multiple network metrics failed to match K-Shortest Path (different combination of metrics means these algorithms are not comparable). Results also showed that Q-Routing was able to calculate paths faster than K-Shortest Path in both static and dynamic networks.

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.

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.

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.

Scalable multicasting with multiple shared trees in software defined networking

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.

Software Defined Networking (SDN) paradigm brings flexibility and programmability to the network management. Deploying a set of SDN controllers to manage a Wide Area Network is challenging as it implies to determine the right number of controllers to install and their locations within the network in addition to minimize the effect of their failures. In this paper, we give a methodology that should be taken into consideration to provide a fault tolerance in SDN networks. In this paper a new model is proposed. The model is called load balancing to support fault tolerance using feedback control for SDNs (LBFTFB). LBFTFB uses feedback control theoretic techniques and uses least squares regression to find the goodness of the model. LBFTFB provides load balancing constraints for several level of backup controllers. Cascading controller failures is one of the popular controller failures. The LBFTFB model reduces the cascading failure problem effect. In order to test the efficiency of the proposed model, a reference model was used. The reference model used is the HyperFlow model. The HyperFlow model is a distributed control plane for OpenFlow. HyperFlow is an application developed on the top of the NOX controller, to enable logically centralized multi-controller architectures. Experimented were conducted to compare the performance of LBFTFB when compared to the HyperFlow. This is an ongoing research. Tentative emulation results show promising controlled results indicating that LBFTFB model outperforms the HyperFlow by 16% on average in terms of packet loss and packet delays.

The control plane of Software Defined Networking (SDN) Featured in the distributed architecture to achieve the scalability, thus the load is divided between the controllers. each controller must have the appropriate load. so, the control plane needs an accurate and beneficial design to meet the load balancing between the controllers. Thus, the switch migration is the most proposed solution for load balancing in SDN. Most of the proposed algorithms are implemented to produce efficient switch migration. In this paper, we propose a secure switch migration scheduling algorithm to enhance the load balancing and achieve the security between the switch and the destination controller. Our algorithm uses an ARIMA prediction approach to predict the load of the controllers and then according to the prediction, the switch migration operation is performed. It reduces the load for the controller which has a very large load by offloading it quickly and it makes the open-flow switch distinguishes if the destination controller is malicious or not. The results show that our algorithm enhances the load balancing compared to [10] and it confirms the security between the switch and the destination controller.

Software Defined Networking (SDN) is a new network design paradigm. SDNs depend on decoupling thecontrol plane from the forwarding plane atthe network switches. SDNs use logically centralized controllers that are responsible for directing the forwarding decisions to the connected switches. In this paper, we design a new model to provide fault tolerance using feedback control to SDNs based on load balancing schemes. The model is called load balancing to support fault tolerance using feedback control for SDNs (LBFTFB). LBFTFB improves the resistance to cascaded failures. Cascading failures cause an enormous increase in the load of the surrounding controllers as a subsequence of the failure of a certain controller. On SDN controller failures, the LBFTFB distributes the load on the failed controller among thesurrounding controllers based on a defined feedback control policy. The LBFTFB model reduces the cascading failure problem when compared to HyperFlow model which is considered as a reference model. The HyperFlow model is a distributed control plane for OpenFlow. HyperFlow model suffers from a dramatic increase in the load of the surrounding controllers as a consequence of the failure of a certain controller. This is an ongoing research. Tentative emulation results are promising and show that the LBFTFB model outperforms the reference model for cascading failures by 14% in terms of packet loss and 17% in terms of packet delays

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 Internet of Things can be perceived as a collection of millions of devices that are connected among each other and with the internet as a connectivity backbone to acquire and share real-time data for providing intelligent services. The tremendous rise in the number of devices requires an adequate network infrastructure to remotely deal with data orchestration. To overcome this issue, a new approach of infrastructure sharing over the cloud among service providers has transpired, with the goal of lowering excessive infrastructure deployment costs. The software-defined networking (SDN) is a networking architecture that enables network operators and users to monitor and manage the network devices remotely and more flexibly by using software that runs on external servers. As SDN and cloud integration improves reliability, scalability, and manageability, this chapter combines cloud infrastructure with SDN. Although SDN-based cloud networks have numerous advantages as mentioned above, there still exist certain challenges that draw the attention of researchers like energy efficiency, security, load balancing, and so on. The work carried out in this chapter is an attempt to address one of the challenging tasks, namely the load balancing, by developing a new multiple-controller load-balancing strategy. The proposed strategy effectively balances the load even if one or more super controllers fail. Furthermore, results are simulated and compared under different operational environments, both with and without the Modified Bully algorithm. The comparison results ensure that the introduced technique exhibits better performance with metrics such as packet loss, packet transmission ratio, and throughput.

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.

The next-generation campus network (CN) is turning into a complex network with a growing number of users, applications, wired and wireless devices. Therefore, to support all the connectivity modes and user demands CN needs to shift from a static, inflexible to a dynamic, flexible, and automated behavior. Software-Defined Networking (SDN) provides flexible network management through its programmable feature and layered architecture. Deployment of multi-controller enhances availability, scalability and brought in a new idea of load sharing between switches and controllers. The CN has thousands of internal, external, and remote users communicating with the network infrastructure. To address the above issues, this article presents SDN based load balancing strategy for campus Networks. Firstly, this article presents an overview of the SDN- based CN framework. Then discuss the conceptual design of a bi-fold load balancing module to shape traffic spikes in SDN-based CN framework. The bi-fold load balancing module consists of dynamic round-robin scheduling for switch load balancing and fractional flow request migration for controller load balancing. This paper proposes a novel load balancing module integrated into SDN-based CN framework.

Load-balancing routing in software defined networks with multiple controllers