Search

Software Defined Networking aims to separate network control and data plane by moving the control logic from network elements into a logically-centralized controller. Using a well-defined, unified control-channel protocol, such as OpenFlow, the controller is able to configure the forwarding behavior of data plane devices. Here, the OpenFlow protocol is translated to vendor- and device-specific instructions that, for instance, manipulate the flow table entries of a switch. In practice, SDN-enabled switches often feature different hardware capabilities and configurations with respect to the number of flow tables, their implementation, and which kind of data plane features they support. This leads to device heterogeneity within the SDN landscape, thereby obstructing the increased scalability and flexibility promised by the SDN paradigm. To overcome this challenge we propose TableVisor, a transparent proxy-layer for the SDN control channel that enables the flexible abstraction of heterogeneous data plane devices into a single emulated data plane switch. In this paper, we extend our previous work by introducing features to integrate modern P4 devices into an existing SDN environment and perform a detailed performance evaluation to quantify the overhead induced by our approach. [ABSTRACT FROM AUTHOR]

With the proliferation of mobile devices, Internet of Things (IoT), and edge commuting, and the advancement of ICT, mobile computing has become the norm, rather than the exception. Current network technologies and distributed system architectures make it difficult to meet users' network trace, and incur high processing costs. To solve this problem, this paper proposes new functional distributed mobility management technologies for the application of the industrial future Internet. This implies the need for an efficient distributed mobility system to apply 5G or TSN (time sensitive network) technologies to the future industry Internet. Thus, it is assumed that various analytical techniques will be applied to provide superior performance in cost analyses, such as location update costs and packet transport costs. In this paper, we propose a cost-effective function-distribution mobility management system that will build a future industrial Internet to meet the needs and convenience of users. It also uses computer simulation to create the mobility provision system, and the electiveness of the cost analysis usage and rental management function models. Software-defined networking in smart factories (SFs) enables them to easily adapt the communication network to changing requirements. Similar to cloud-based systems, such SFs could be seen as producing clusters that could be rented and configured as needed. To achieve the required flexibility, the SF network uses software defined networking combined with network function virtualization. Despite the fact that the technology is not yet ready for deployment in today's manufacturing networks, a novel network architecture for SFs based on software-defined networking and network virtualization is here proposed, to support smart services, especially for Industry 4.0. [ABSTRACT FROM AUTHOR]

The software‐defined network (SDN) is one of the network architectures, in which the data plane and control plane is divided from each other, and the network can be handled using a sensibly centralized controller and this method is adopted to reconfigure the wireless sensor network automatically. In this article, to implement the SDN in MANET, in which control nodes can be chosen in SDN dynamically for the activation of MANET function to allocate the works to other mobile nodes to the base station. However, in the field of mobile ad hoc networks, the network lifetime, and battery lifetime is one of the major problems and the energy consumption can play a significant rule for the transmission of data in the SDN. Therefore, in this article, particle swarm optimization (PSO) based CGSR (cluster‐head gateway switch routing protocol) algorithm with fuzzy rules is proposed to increase the network lifetime of battery powered mobile nodes by reducing the energy consumptions of each node in software‐defined MANET. In this proposed method, a routing method that can permit various mobile nodes with low battery power to transmits the data from source node to base station. We design a PSO based CGSR routing protocol by selecting the routing mobile nodes using fuzzy rules for packet transmission. In CGSR process, the formation of cluster and selection of cluster head is executed depending on the particle swarm optimization method. This proposed routing protocol can be used to enhance the battery lifetime by extension of the network lifetime with numerical analysis for efficient route node selection. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design and prototype implementation of the SELFNET fifth‐generation (5G) mobile edge infrastructure. In line with the current and emerging 5G architectural principles, visions, and standards, the proposed infrastructure is established primarily based on a mobile edge computing paradigm. It leverages cloud computing, software‐defined networking, and network function virtualization as core enabling technologies. Several technical solutions and options have been analyzed. As a result, a novel portable 5G infrastructure testbed has been prototyped to enable the preliminary testing of the integrated key technologies and to provide a realistic execution platform for further investigating and evaluating software‐defined networking– and network function virtualization–based application scenarios in 5G networks. [ABSTRACT FROM AUTHOR]

Software-defined networking introduces opportunities to optimize the Internet Service Provider's network and to improve client experience for the Video-on-Demand applications. Recent studies on SDN frameworks show that traffic engineering methods allow a fair share of bandwidth between adaptive video streaming clients. Additionally, ISPs can make better estimations of bandwidth and contribute to the bitrate selection for the clients. This study focuses on another aspect of network assistance in video delivery: CDN server selection. In a typical framework where the ISP contributes to the CDN selection, the video provider and the network provider interfaces are merged together. Clients connect to the ISP to get the best CDN server candidate for a given video. This exposes client requests to the ISP. However, video providers have been investing large resources for encrypted video provisioning to preserve their client's information from third parties, especially network providers. The typical approach is not practical due to privacy concerns. In this study, we present a framework called PPNet to allow CDN-ISP collaboration while preventing the ISP's access to the video request and availability information. Our framework introduces an isolation between the video provider's and the ISP's web interfaces. Clients connect to both of the interfaces and deliver information on a need-to-know basis. As a second contribution, PPNet introduces a practical optimization method for CDN selection. Real-time data collection capabilities of a typical OpenFlow network is used as the input for optimization. Congestion-awareness has been the priority. To adapt for changing network conditions, capability of utilizing multiple servers simultaneously for a single video is introduced. Instead of directing each video client into a CDN node, the proposed system performs request routing per video segment. Finally, we present a system prototype of PPNet and show that our multiple-host adaptive streaming method introduces a significant improvement in quality of experience when compared to the state of the art. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) reshapes the ossified network architectures, by decoupling the control plane and data plane. Due to such a decoupling, SDN assumes that rules issued by the control plane are always correctly enforced by the data plane. However, this assumption breaks as an adversary can prevent the data plane from enforcing the rules, by exploiting the vulnerabilities of switch OS and control channel. The serious consequence is that packets may deviate from their original paths, thereby violating critical security policies like access control. To this end, this paper introduces rule enforcement verification (REV), which enables the controller to check whether switches have correctly enforced the rules that it issues. Since using message authentication code (MAC) can incur heavy switch-to-controller traffic, we propose the compressive MAC, which lets switches compress MACs before reporting to the controller, thereby significantly reducing the bandwidth cost. Finally, we propose a heuristic flow selection algorithm, which allows the controller to verify much less flows for rule coverage. We implement REV based on Open vSwitch with DPDK, and use experiments to show: (1) by using compressive MAC, REV achieves a 97% reduction in switch-to-controller traffic, and an $8\times $ increase in verification throughput; (2) by using the heuristic flow selection algorithm, REV can reduce the number of flows to verify by 40%–60%. [ABSTRACT FROM AUTHOR]

Data center networks employ parallel paths to perform load balancing. Existing traffic splitting schemes propose weighted traffic distribution across multiple paths via a centralized view. An SDN controller computes the traffic splitting ratio of a flow group among all the paths, and implements the ratio by creating multiple rules in the flow table of OpenFlow switches. However, since the number of rules in TCAM-based flow table is limited, it is not scalable to implement the ideal splitting ratio for every flow group. Existing solutions, WCMP and Niagara, aim at reducing the maximum oversubscription of all egress ports and reducing traffic imbalance, respectively. However, the transmission time of flow groups, which measures the quality of cloud services, is sub-optimal in existing solutions that ignore heterogeneous network bandwidth. We propose and implement NAMP, a multipathing scheme considering the network heterogeneity, to efficiently optimize the transmission time of flow groups. Experimental results show that NAMP reduces the transmission time by up to 45.4% than Niagara, up to 50% than WCMP, and up to 60% than ECMP. [ABSTRACT FROM AUTHOR]

A network paradigm called the Software-Defined Network (SDN) has recently been introduced. The idea of SDN is to separate the control logic from forwarding devices to enable a centralized control platform. However, SDN is still a distributed and asynchronous system: events can be triggered by any network entity, while messages and packets are prone to arbitrary and unpredictable transmission delays. Moreover, the absence of a global temporal reference results in a broad combinatorial range space of event order. During network updates, an out-of-order execution of events may result in a deviation from desirable consistent network update properties, leading, for example, to forwarding loops and forwarding black holes, among others. In this paper, we introduce a study of the Transient Forwarding Loop (TFL) phenomenon during SDN updates; for this, we define a formal model of the TFL based on causal dependencies that capture the conditions under which it may occur. Based on this model, we introduce an algorithm that ensures the causal dependencies of the system oriented toward TFL-free SDN updating. We formally prove that it is sufficient to ensure the causal dependencies in order to guarantee TFL-free network updates. Finally, we analytically evaluate our algorithm and discuss how it outperforms the state-of-the-art in terms of updating overhead. [ABSTRACT FROM AUTHOR]

In large-scale software-defined networks (SDNs), multiple controllers are deployed. Each controller has a logically centralized vision of the network that is used to manage a set of switches. In SDN, a challenge known as controller placement problem arises which is very important to specify the number of controllers that are needed and where they should be deployed. These specifications affect the performance of the network. Meanwhile, the assignment of switches to the controllers plays key role in the quality of solution in this problem. However, recent studies focus more on simply assigning switches to their closest controllers based on propagation delay between controller and switches. In this paper, a new controller placement framework is designed which considers both control plane architecture and relation between control and data planes. This framework is considered as a multi-objective optimization model with two objective functions to minimize the flow setup time and inter-controller latency. Furthermore, we propose a new model for flow setup time function that considers all affected metrics in the placement. To solve the framework, a multi-objective algorithm called non-dominated sorting moth flame controller placement optimizer is designed. To this end, we adapt the best–worst multi-criteria decision-making method considering three metrics, namely hop count, propagation latency and link utilization, to assign switches to controllers. A heuristic approach is also used to assign a path between switch and its controller using above three metrics and path reliability. We run theses three models iteratively to find the best location for controllers, the best switch assignment to controller and also find the best route in the network. We compare our proposed framework with other models using expected path loss and link load balancing metrics. Our performance evaluations on real wide area network topologies show the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mobility-aware task offloading scheme, named as Soft-VAN, with an aim to minimize task computation delay in a software-defined vehicular network. The proposed scheme consists of two phases — fog node selection and task offloading. In the first phase, we formulate an integer linear program (ILP), and solve the problem to get optimal number of fog nodes required for a given network. In the task offloading phase, we formulate an optimization problem to minimize overall delay in task computation, while considering associated constraints. As finding optimal solution to the problem is NP-hard, we propose a greedy heuristic approach in two phases — task offloading and computed task downloading — to solve it in polynomial time. The greedy solution for offloading takes into account network delay, flow-rule capacity, and link utilization. On the other hand, the solution for computed task downloading considers vehicle's mobility in addition to the parameters associated with the offloading decisions. Experimental results show that the proposed scheme, Soft-VAN, is capable of enhancing the performance approximately by 30%, 45%, and 50% in terms of delay compared to state-of-the-art schemes — Detour, DAGP, and SD2O, respectively. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is fundamentally changing the way networks operate, enabling programmable and flexible network management and configuration. As the de facto standard southbound interface of SDN, OpenFlow defines how the control plane interacts with the data forwarding plane. In OpenFlow, flow tables play a significant role in packet forwarding. However, the size of the flow table is limited due to power, cost, and silicon area constraints and capacity-limited tables cannot hold all of the active flows in medium-to-large-scale SDN networks. Thus, when a flow table reaches capacity, an intelligent eviction strategy, which efficiently manages the limited flow table resource, is critical. In this paper, we propose Smart Table EntRy Eviction for OpenFlow Switches (STEREOS), which uses machine learning to classify flow entries as active or inactive and forms the basis for intelligent eviction. Trace-driven simulations demonstrate that STEREOS increases flow table usage by more than 50% and reduces incorrect flow entry evictions by up to 78%, compared with the dominant Least Recently Used eviction policy. Moreover, packet-level simulations of a datacenter network demonstrate that STEREOS can greatly reduce the control overhead, increase overall network throughput by 19%, and reduce packet loss rate by 70%. [ABSTRACT FROM AUTHOR]

Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion. [ABSTRACT FROM AUTHOR]

Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure. [ABSTRACT FROM AUTHOR]

Software-defined networking is a promising technology for constructing communication networks where the network management is the software that configures network devices. This contrasts with the traditional point of view where the network behaviour is updated by manual configuration uploading to devices under control. The software controller allows dynamic routing configuration inside the net depending on the quality of service. However, there must be a proof that ensures that every network flow is secure, for example, we can define security policy as follows: confidential nodes can not send data to the public segment of the network. The paper shows how this problem can be solved by using a semantic security model. We propose a method that allows us to construct semantics that captures necessary security properties the network must follow. This involves the specification that states allowed and forbidden network flows. The specification is then modeled as a decision tree that may be reduced. We use the decision tree for semantic construction that captures security requirements. The semantic can be implemented as a module of the controller software so the correctness of the control plane of the network can be ensured on-the-fly.

Software‐defined networking (SDN) already has an established presence in the networking industry as an emerging paradigm for designing network architectures, decoupling the control from the forwarding plane and offering a centralised way of network management. Cloud and enterprise infrastructure engineers have already adopted such techniques for building up their environments, deliberating the cost and flexibility advantages gained from this technology. However, smaller companies may choose a progressive replacement of their equipment due to operational and budget constraints. Hence, mixing equipment could lead up to hybrid SDN environments making network management even more complex. In this study, an event‐based generic topology discovery algorithm, capable of providing an efficient packet discovery mechanism using existing protocols in SDN networks, is proposed. For testing and evaluation purposes, address resolution protocol and link layer discovery protocol are being used to extract the information needed and reconstruct the topology to the controller. Implementation is accomplished using Internet Engineering Task Force's ForCES framework.

Software-defined networking (SDN) provides flexible network control, which has enabled new, more complex network control mechanisms. These approaches impose high demands on flow updates in OpenFlow switches. Existing SDN controllers and firmware have been optimized for high-speed network updates, but specific switch implementations differ in their behavior. Previous optimization schemes for updates ignore the diversity of instruction types and switch behavior. In this paper, we present RuleTailor, an efficient, measurement-based optimization framework for SDN flow updates, to overcome these limitations. In contrast to other measurement-based optimization frameworks, RuleTailor uses new techniques, such as instruction type transformation, pseudo deletion, and match field distance, which can work in both the control plane and the data plane. Our evaluation shows that RuleTailor achieves a performance of fewer than 12 milliseconds per update in a content-addressable flow table with 1k entries, outperforming the state-of-the-art measurement-based framework, Tango, by a factor of 10. [ABSTRACT FROM AUTHOR]

Rstercaecntt trends have shown a migration of software from local machines to server-based services. These servicebased networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity. [ABSTRACT FROM AUTHOR]

Software-Defined Networks (SDNs) are developed to compensate the complicated function of the controlling parts of the given network elements and making the scalability easier. In SDNs, the controlling operations are implemented by a logically centralized controller, where the occurrence of failure resulting in separation of the control and data plane is inevitable. Since the reliability of centralized controllers is low due to their being of the single point of failure, the focus of this study is to improve the reliability of the SDNs with distributed controllers. In this article, the wide networks are partitioned into smaller subnetworks, where each one is being controlled by a controller in order to reduce the failure effect(s). In each subnetwork, its reliability is calculated by considering the number and degree of nodes and the loss rate of the links and then is transmitted among the controllers through the Leader Election and Dijkstra Algorithms. Afterward, the controller with the highest reliability rate is considered as the coordinator through the newly proposed Coordinator Finder Algorithm. In practice, when a controller fails, the coordinator would choose the appropriate controller for its subnetwork in a transitory manner, where the fault tolerance and accuracy would be improved and the latency would reduce. A newly designed Colored Petri-Net is applied to verify this proposed method. [ABSTRACT FROM AUTHOR]

Internet service providers are offering shared data plans where multiple users may buy and sell their overage data in a secondary market managed by the ISP. We propose a game-theoretic approach to a software-defined network for modeling this wireless data exchange market: a fully connected, non-cooperative network. We identify and define the rules for the underlying progressive second price (PSP) auction for the respective network and market structure. We allow for a single degree of statistical freedom—the reserve price—and show that the secondary data exchange market allows for greater flexibility in the acquisition decision making of mechanism design. We have designed a framework to optimize the strategy space using the elasticity of supply and demand. Wireless users are modeled as a distribution of buyers and sellers with normal incentives. Our derivation of a buyer-response strategy for wireless users based on second price market dynamics leads us to prove the existence of a balanced pricing scheme. We examine shifts in the market price function and prove that our network upholds the desired properties for optimization with respect to software-defined networks and prove the existence of a Nash equilibrium in the overlying non-cooperative game.

In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.

The increasing complexity of modern day networked applications and the massive demand on the Internet resources has reignited interest and concern in the underlying networking infrastructures and their ability to cope with such complexity and adapt to the demands of the business applications, particularly where such applications require a high degree of robustness and reliability. As a result, software-defined networking has emerged as a promising approach to the definition of network architectures that could carry a high degree of adaptability and robustness reminiscent of the future Internet. Fault tolerance and network updates are considered two of the current research challenges that hamper the growth of software-defined networking in this area. Therefore, this paper represents a step toward tackling these two issues in the context of single link failures. Our main contribution lies in the definition of new algorithms that aim to enhance the problem of finding alternative paths in large-scale networks with minimal cost and time-to-update factors. The new solution aims at increasing the efficiency of flow operation reduction during link failures. We evaluate our framework and show how its implementation results in improved efficiency.

The growing energy consumption of communication networks has attracted the attention of the networking researchers in the last decade. In this context, the new architecture of Software-Defined Networks (SDN) allows a flexible programmability, suitable for the power-consumption optimization problem. In this paper we address the issue of designing a novel distributed routing algorithm that optimizes the power consumption in large scale SDN with multiple domains. The solution proposed, called DEAR (Distributed Energy-Aware Routing), tackles the problem of minimizing the number of links that can be used to satisfy a given data traffic demand under performance constraints such as control traffic delay and link utilization. To this end, we present a complete formulation of the optimization problem that considers routing requirements for control and data plane communications. Simulation results confirm that the proposed solution enables the achievement of significant energy savings.

Software-defined networks (SDN) is an evolution in networking field where the data plane is separated from the control plane and all the controlling and management tasks are deployed in a centralized controller. Due to its features regarding ease management, it is emerged in other fields such as cloud and fog computing in order to manage asymmetric communication across nodes, thus improving the performance and reducing the power consumption. This study focused on research that were conducted in SDN-based clouds and SDN-based fogs. It overviewed the important contributions in SDN clouds in terms of improving network performances and energy optimization. Moreover, state-of-the-art studies in SDN fogs are presented. The features, methods, environment, dataset, simulation tool and main contributions are highlighted. Finally, the open issues related to both SDN clouds and SDN fogs are defined and discussed.

Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and deploying their network infrastructures, by giving the possibility to service/application providers to reconfigure and programmatically perform actions on the network. Accordingly, the dynamic management of the data center networks requires complex operations to ensure high availability and continuous reliability in order to guarantee full functionality of the virtualized resources. In this context, simulator-based approaches are helpful for planning and evaluating the deployment of the cloud data center networking, but existing cloud simulators have several limitations: they have too high overhead for wide-scale data center networks, complex configuration, and too abstract deployment models. For these motivations, we propose DCNs-2, a novel extension for the Ns-2 simulator, as a valid solution to efficiently simulate a cloud network infrastructure, with all the involved entities, such as switches, physical/virtual machines, and racks. The proposed solution not only makes configuration easier, but through extensive tests, we show that its execution overhead is limited to less than 130 MB of memory and the execution time is acceptable even for very wide-scale and complex deployment environments. [ABSTRACT FROM AUTHOR]

With an increasing demand for resilience in software-defined networks (SDN), it becomes critical to minimize service recovery delay upon route failures. Fast reroute (FRR) mechanisms are widely used in IP and MPLS networks by computing the recovery path before a failure occurs. The centralized control plane in SDN can potentially enhance path computation, so that FRR path computation can better scale in SDN than in traditional networks. However, the traditional FRR path computation algorithms could lead to a poor performance in large-scale SDN. The problem can become more severe for a highly dynamic network, which often sees dozens of failures or configuration changes in any single day. We propose a new algorithm that exploits pruned searching to quickly compute recovery paths for all-pair switches/hosts upon a link failure. For applications requiring stringent path robustness levels, we also extend this algorithm to quickly find the shortest guaranteed-cost path, which ensures that the recovery path used upon on-path link failures has the minimum cost. Compared with traditional solutions, our evaluations show that our algorithm is about 8 ~ 81 times faster than the practical implementation, 1.93 ~ 3.11 times faster than the state-of-the-art solution. Our results also show that the shortest guaranteed-cost path can reduce the cost of the recovery path significantly. Moreover, we design a prototype to show how to deploy our algorithm in an OpenFlow network. [ABSTRACT FROM AUTHOR]

To address the challenges of autonomous capability enhancement in a smart home scenario, in this paper, we present a context-driven smart home control mechanism (SHCM) following software-defined network (SDN) design principles and a context cognition process. SHCM has three SDN-based layers: a control plane, a fog computing plane, and a data plane. We integrate a machine learning (ML) algorithm and an ontology model into the context cognition process, which will be leveraged to enhance the context-awareness-enabled automation level of smart home control systems. In the control plane, a controller adopts a ML-based tool to make connotative clustering and association rules via mining multiattribute context features inherent in diverse sensing applications, and utilizes an ontology model to automate integrated context management. Additionally, the fog computing plane applies edge-computing-supported context middleware to perform compressive sensing (CS)-based cross-layer context fusion. Furthermore, smart home devices implement context feedback in the data plane instructed by context-driven control strategies, which are mapped into the parameter matrix and matching rules in the lightweight flow-table mode. The effectiveness of this proposed control mechanism is validated by experiments using a context-oriented smart home prototype platform, which implements a closed-loop context-oriented feedback control from cognition-deduced knowledge generation to knowledge-driven cooperation in a cyber-physical smart home scenario. It is observed that the control mechanism can improve smart home automation and outperform baseline schemes. [ABSTRACT FROM AUTHOR]

In software-defined networking (SDN), high-level abstractions typically offer a useful means to avoid writing network applications and policies on lower levels. However, abstractions are typically developed for a specific use case, which in turn results in an abundance of existing abstractions for different networking tasks. As a consequence, orchestrating these abstractions to implement a common network policy becomes an arduous task. To address this challenge, plain data representations of the network and its control infrastructure have been proposed recently, which offer programmable ad-hoc abstractions to administrators. However, these frameworks suffer from quite complex programming requirements and impractical performance in terms of latency, as they are based on relational database engines. In this paper, we address these shortcomings by introducing Gavel, an SDN controller that at its heart facilitates a plain data representation based on a graph database. By exploiting the native graph support of the database engine, Gavel significantly eases application and policy writing. Additionally, we show by experimental evaluation of several typical applications on multiple different topologies that Gavel offers significant performance improvements over the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Most of the enterprises are transforming their conventional networks into Software-Defined Network (SDN) to avail the cost efficiency and network flexibility. But recent attacks and security breaches against SDNs expose the security weakness of the technology. Distributed Denial of Service (DDoS) is the most common attack launched against various SDN architecture layers. Hence, DDoS has been claimed to be the most dangerous attack and threat to SDN. The existing mitigation techniques are traffic volumetric methods, entropical methods and traffic flow analysis methods. They depend on traffic sampling to achieve truly inline against DDoS detection accuracy in real time. However, traffic sampling-based methods are expensive with chances for incomplete approximation of underlying traffic patterns being very high. Early detection of DDoS attack in the controller is critical and requires highly adaptive and accurate methods. In this paper, an effective and accurate DDoS detection method using Lion optimization algorithm is proposed. The proposed detection technique is robust enough to detect DDoS attack within the least magnitude of attack traffic. Further, to evaluate the performance, the proposed method is compared with the state-of-the-art techniques. The outcome of this paper is current method limitation and scope for improvement depicted from overall study and analysis. The experimental results have proved that the proposed method outperforms the existing state-of-the-art methods with 96% accuracy. [ABSTRACT FROM AUTHOR]

Abstract With the increasing conflict between population growth and limited environmental resources, building intelligent and efficient data transmission networks is crucial to cleaner production in smart and sustainable cities. More and more people are eager to exchange all kinds of information in urban life through an efficient network. Therefore, designing a smart data distribution and transmission mechanism can not only improve people's quality of life but also pave the way for reducing environmental pollution. Optical networks have been widely used in the core network due to high-capacity characteristics. However, in inter-data-center (Inter-DC) optical networks, the coexisted heterogeneous optical devices and multi-granularity network resources bring new challenges to network management and traffic scheduling. Recently, software-defined network (SDN) and network function virtualization (NFV) have been introduced to enable the unified control for improving network agility and automation. Thus for software-defined multi-granularity Inter-DC optical networks, we propose a list of online service provisioning strategies. Moreover, in terms of the network controller, the novel routing applications are also developed by extended OpenFlow protocol. Finally, we experimentally demonstrate the overall feasibility of proposed solutions in a semi-practical platform. Numerical simulations conducted to quantitatively evaluate the algorithm efficiency also verify the superiorities of improving spectrum efficiency and reducing service blocking probability. [ABSTRACT FROM AUTHOR]

Multicasting is a fundamental functionality of many network applications, including online conferencing, event monitoring, video streaming, and so on. To ensure reliable, secure, and scalable multicasting, a service chain that consists of network functions (e.g., firewalls, intrusion detection systems, and transcoders) usually is associated with each multicast request. We refer to such a multicast request with service chain requirement as an network function virtualization (NFV)-enabled multicast request. In this paper, we study NFV-enabled multicasting in a software-defined network (SDN) with an aim to maximize network throughput while minimizing the implementation cost of admitted NFV-enabled multicast requests, subject to network resource capacity, where the implementation cost of a request consists of its computing resource consumption cost in servers and its network bandwidth consumption cost when routing and processing its data packets in the network. To this end, we first formulate two NFV-enabled multicasting problems with and without resource capacity constraints and one online NFV-enabled multicasting problem. We then devise two approximation algorithms with an approximation ratio of $2M$ for the NFV-enabled multicasting problems with and without resource capacity constraints, if the number of servers for implementing the service chain of each request is no greater than a constant $M$ (≥1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an efficient heuristic, and for the special case of dynamic request admissions, we devise an online algorithm with a competitive ratio of $O(\log n)$ for it when $M=1$ , where $n$ is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and outperform existing heuristics. [ABSTRACT FROM AUTHOR]

Abstract Visible light communication (VLC) is a wireless technology complementary to well-understood radio frequency (RF) communication that is promising to help alleviate the spectrum crunch problem in overcrowded RF spectrum bands. While there has been significant advancement in recent years in understanding physical layer techniques for visible light point-to-point links, the core problem of developing efficient networking technology specialized for visible-light networks is substantially unaddressed. This article discusses the current existing techniques as well as the main challenges for the design of visible-light ad hoc networks - referred to as LANETs. The paper discusses typical architectures and application scenarios for LANETs and highlights the major differences between LANETs and traditional mobile ad hoc networks (MANETs). Enabling technologies and design principles of LANETs are analyzed and existing work is surveyed following a layered approach. Open research issues in LANET design are also discussed, including long-range visible light communication, full-duplex LANET MAC, blockage-resistant routing, VLC-friendly TCP and software-defined prototyping, among others. [ABSTRACT FROM AUTHOR]

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance and creates bottlenecks. To minimize the flow entry update time, a dependency graph, a kind of directed acyclic graph (DAG), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about $100 \times $ faster than state-of-the-art solutions with a flow table of $1k$ size. [ABSTRACT FROM AUTHOR]

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-defined network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. We conclude by discussing near-term experimental efforts that can realize SDN's principles for quantum communication. [ABSTRACT FROM AUTHOR]

The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large-scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal–dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale. [ABSTRACT FROM AUTHOR]

The killer features of the next-generation 5G mobile standard, including mobile edge computing and network slicing, will be very difficult to support with traditional fixed-function network appliances. Rather, the 5G core will depend on programmable switches, which allow packet processing functionality to be reconfigured on the fly in order to deploy virtualized network functions and service chains instantaneously. With 5G on the close horizon, it has become crucial to identify the price for programmability in the software data plane, considering the expected complexity and scale of the next-generation mobile core. In this paper, we report on a multi-year data-plane scalability study we have conducted for a large mobile vendor. Our results paint a rather pessimistic picture on the current landscape of the programmable software data plane. We find that the prominent programmable switches either do not provide all the features necessary to implement 5G telco pipelines efficiently or struggle to meet the scale, and the performance operators have come to expect from conventional fixed-function appliances. The only exception, ESwitch, remains proprietary. We call for further work on data-plane scalability and sketch some directions for future research. [ABSTRACT FROM AUTHOR]

Decentralized orchestration of the control plane is critical to the scalability and reliability of software-defined network (SDN). However, existing orchestrations of SDN are either one-off or centralized, and would be inefficient the presence of temporal and spatial variations in traffic requests. In this paper, a fully distributed orchestration is proposed to minimize the time-average cost of SDN, adapting to the variations. This is achieved by stochastically optimizing the on-demand activation of controllers, adaptive association of controllers and switches, and real-time request processing and dispatching. The proposed approach is able to operate at multiple timescales for activation and association of controllers, and request processing and dispatching, thereby alleviating potential service interruptions caused by orchestration. A new analytic framework is developed to confirm the asymptotic optimality of the proposed approach in the presence of non-negligible signaling delays between controllers. Corroborated from extensive simulations, the proposed approach can save up to 73% the time-average operational cost of SDN, as compared to the existing static orchestration. [ABSTRACT FROM AUTHOR]

In this paper, we present a topology-aware network coding (TANC) technique for wireless multicast systems aiming at reducing packet retransmissions in a lossy propagation environment. In addition to radio channel impacts, packet loss may result from traffic congestion, thereby yielding different retransmission requests. In such a wireless network coding multicast environment, what is the best packet combination to minimize the packet retransmissions for a number of users? We make progress on this problem by taking advantage of the network topology information to select the appropriate lost encoded packets in the encoding phase. We leverage the capability of software-defined networking controller to construct the global network topology in the proposed TANC scheme. Experimental results show that the proposed TANC scheme can reduce 52.4% of packet retransmissions compared to the existing random linear network coding in different environments, including various numbers of uncoded packets, network nodes, and packet loss probabilities. [ABSTRACT FROM AUTHOR]

The software-defined networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper, we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path-based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%. [ABSTRACT FROM AUTHOR]

Supporting on-line and adaptive traffic engineering in software-defined networks entails the fast, robust control message forwarding from software-defined switches to the controller(s). In-band control using the existing infrastructure is cost-efficient, but imposes a substantial barrier to timely transmissions of control messages. Also, due to the limited computational capability of a single controller, only the use of multiple controllers is practically viable for large-scaled networks. Therefore, in this paper, the optimal software-defined network planning is investigated with multi-controllers. First, the network planning problem is formulated as a nonlinear multi-objective optimization, which aims to simultaneously minimize the number of controllers and the control traffic delay for each switch. This planning problem is then partitioned into two sub-problems, i.e., multi-controller placement and control traffic balancing, which are respectively solved by the proposed fast-convergent algorithms. Furthermore, an adaptive feedback control mechanism is proposed to iteratively work out the two sub-problems and enable the dynamic network replanning, subject to the time-varying traffic volume and network topology. Simulations validate the adaptivity of our control scheme, which significantly reduces delay with maximum throughput for control flows, brings minimal impact to normal data flows, and requires the minimum controllers. [ABSTRACT FROM AUTHOR]