Performance Optimization Strategies for Big Data Applications in Distributed Framework

The fundamental role of the software defined networks (SDNs) is to decouple the data plane from the control plane, thus providing a logically centralized visibility of the entire network to the controller. This enables the applications to innovate through network programmability. To establish a centralized visibility, a controller is required to discover a network topology of the entire SDN infrastructure. However, discovering a network topology is challenging due to: 1) the frequent migration of the virtual machines in the data centers; 2) lack of authentication mechanisms; 3) scarcity of the SDN standards; and 4) integration of security mechanisms for the topology discovery. To this end, in this paper, we present a comprehensive survey of the topology discovery and the associated security implications in SDNs. This survey provides discussions related to the possible threats relevant to each layer of the SDN architecture, highlights the role of the topology discovery in the traditional network and SDN, presents a thematic taxonomy of topology discovery in SDN, and provides insights into the potential threats to the topology discovery along with its state-of-the-art solutions in SDN. Finally, this survey also presents future challenges and research directions in the field of SDN topology discovery.

Software defined network (SDN) can effectively improve the performance of traffic engineering and will be widely used in backbone networks. Therefore, new energy-saving schemes must take SDN into consideration; this action is extremely important owing to the rapidly increasing energy consumption in telecom and Internet service provider (ISP) networks. Meanwhile, the introduction of SDN in current networks must be incremental in most cases, for technical and economic reasons. During this period, operators must manage hybrid networks in which SDN and traditional protocols coexist. In this study, we investigate the energy-efficient traffic engineering problem in hybrid SDN/Internet protocol (IP) networks. First, we formulate the mathematical optimization model considering the SDN/IP hybrid routing mode. The problem is NP-hard; therefore, we propose a fast heuristic algorithm named hybrid energy-aware traffic engineering (HEATE) as a solution. In our proposed HEATE algorithm, the IP routers perform shortest-path routing by using distributed open shortest path first (OSPF) link weight optimization. The SDNs perform multi-path routing with traffic-flow splitting managed by the global SDN controller. The HEATE algorithm determines the optimal setting for the OSPF link weight and the splitting ratio of SDNs. Thus, the traffic flow is aggregated onto partial links, and the underutilized links can be turned off to save energy. Based on computer simulation results, we demonstrate that our algorithm achieves a significant improvement in energy efficiency in hybrid SDN/IP networks.

Traffic engineering in hybrid SDN networks with multiple traffic matrices

This paper proposes a Traffic Engineering (TE) scheme for routing in a target network topology that supports OpenFlow using a metric called congestion contribution and Software-Defined Networking (SDN). According to the network traffic volume has been increasing dramatically, it is clear that the congestion, which is caused by the imbalance of the traffic flow, can be occurred. Furthermore, an Interior Gateway Protocol (IGP) that is used for an Autonomous System (AS) also can cause congestion because it uses snapshot information of network topology to make the shortest path-based routing table. Due to the reasons mentioned above, much research on TE has been conducting to enhance the network performance. However, as an example of TE, the Multi-Protocol Label Switching (MPLS) using Resource Reservation Protocol (RSVP) is not flexible in reconfiguring the labeled path, and it has resource overhead that cannot be ignored. Thus, we propose a TE scheme using SDN to overcome the limitations in the target network. In order to show the feasibility of the proposed scheme, TE application predicts the bandwidth utilization for network devices in SDN networks. It also determines the near-optimized routing path for the maximization of the average bandwidth utilization with avoiding the congestion nodes. Our scheme can configure network devices (e.g., SDN switches) along the paths of traffic flows so that the SDN network can have maximum bandwidth utilization. Through experiments, it is shown that our proposed scheme can utilize the average bandwidth of the whole topology up to 66%.

Network Virtualization (NV) and Software Defined Networking (SDN) bring new business opportunities for network providers allowing them to offer the network as a service over their network infrastructure. One challenge for network providers is to provision virtual SDN services for multiple tenants while satisfying QoS and traffic engineering (TE) objectives of the higher priced SDN services, allocating the network resources cost effectively and managing the virtual services that span over customer premises, wide area and cloud domains. In this paper, we describe an algorithmic framework that includes QoS management and traffic-engineering methods for managing virtual services over large SDN networks. The algorithmic framework is part of an intelligent service management architecture which employs optimization algorithms, machine learning and artificial intelligence techniques into the management of virtual services over SDN networks.

In Wireless Sensor Network (WSN), resource optimization is one of the challenging issues. It improves the network performance. This can be achieved with the help of a new networking technology, called Software Defined Networking (SDN). The concept of SDN is to decouple the control plane from the forwarding plane of a network to improve the performance of the networking applications. Traffic Engineering (TE) is a domain of network applications that manages the network traffic, and try to optimize the network resources. SDN offers multiple features to support TE applications. SDN combined with WSN forms a new networking domain called Software Defined WSN (SDWSN). Since WSN has resource-constrained nodes, resource optimization is a need in WSN. This paper proposes an optimized link utilization based approach for TE in SDWSN. The proposed approach discusses about a traffic optimization algorithm, called Optimized Link Allocation based TE (OLA-TE). OLA-TE is implemented at the controller node and helps to improve the overall network utilization. The SDN sensor nodes find the optimized path for traffic routing with the help of controller node. The proposed approach is compared with the traditional WSN approach. IBM CPLEX Optimization Studio and MATLAB are used for validation purposes. The CPLEX results assure a network flow-cost optimization of about 10%. The results of MATLAB show that despite having extra control traffic on network, the network performance gets improved over the period of time.

Recently, the usage of Internet through mobile devices has surpassed the access through wired network. It makes extremely difficult for the network operators to manage their network in resource efficient manner. Software Defined Networking (SDN), a key 5G enabling technology, makes network management easier for the network operators through logically centralized control plane. One of the benefits of SDN is specific treatment for different network traffic type, however it requires an efficient traffic classification solution. Comprehensive research has been done on deep packet inspection or machine learning based traffic classification methods which are unsuitable for delay intolerant networks due much overhead. This paper proposes a simple traffic classification mechanism using different IP addresses which are assigned to Mobile Node (MN). Different applications in MN uses one of the assigned IP addresses based on the type of network traffic they generate. Centralized SDN controller uses these IP addresses to classify the network traffic. This method is useful for various network functions such as load balancing, security and mobility. In this paper we take mobility as a use case and through emulation results establish that traffic classification significantly improves MN mobility performance.

Energy efficiency is one of the key requirements in Wireless Sensor Networks (WSNs). In order to optimize energy usage at sensor nodes, this paper proposes a new WSN architecture that relies on concepts of Software Defined Networking (SDN). Since SDN is a relatively new technology, originally envisioned for wired networks, it can't be expected to get immediately adopted in WSN domain, regardless of potential benefits. For this reason, we consider scenario where SDN nodes coexist with traditional sensor nodes, and propose a new routing algorithm for SDN controller that prolongs the WSN lifetime even when small percentage of SDN nodes is deployed.

Software defined network (SDN) with disjoint control and data plane enables the controller to address network requirement better. A significant issue in such networks is how the network resources are allocated by the controller based on the demand, for instance, allocation of path with less delay, less error rate and so forth. The controller allocate such network resources by enabling link discovery between switches. SDN utilizes protocol like link layer discovery protocol (LLDP) for links to be identified in OpenFlow devices. The detection of links in OpenFlow devices requires a huge number of PACKET OUT (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</inf> ) messages from the controller to be sent across every active interfaces of the switch which might limit the controller performance. This paper introduces a Broadcast based Link Discovery (BBLD) scheme which lowers messages required to obtain the link discovery in OpenFlow switches (OFS’s) by dispatching a solitary P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</inf> from the SDN controller (unlike a unicast message from the controller in case of LLDP) to any OpenFlow switch (OFS), which then circulates the same to the nodes successively. Experiments performed with Mininet reveal that messages required to obtain similar link discovery are reduced by an average of (28%-42.4%) under varied set of topologies.

Software Defined Networking (SDN) is a new networking paradigm, with a great potential to increase network efficiency, ease the complexity of network control and management, and accelerate the rate of technology innovation. One of the core concepts of SDN is the separation of the network's control and data plane. The intelligence and the control of the network operation and management, such as routing, is removed from the forwarding elements (switches) and is concentrated in a logically centralised component, i.e. the SDN controller. In order for the controller to configure and manage the network, it needs to have up-to-date information about the state of the network, in particular its topology. Consequently, topology discovery is a critical component of any Software Defined Network architecture. In this paper, we evaluate the cost and overhead of the de facto standard approach to topology discovery currently implemented by the major SDN controller frameworks, and propose simple and practical modifications which achieve a significantly improved efficiency and reduced control overhead. We have implemented our new topology discovery approach on the widely used POX controller platform, and have evaluated it for a range of network topologies via experiments using the Mininet network emulator. Our results show that our proposed modifications achieve an up to 45% reduction in controller load compared to the current state-of-the-art approach, while delivering identical discovery functionality.

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

Traffic engineering (TE) can balance the traffic in the network to reduce network congestion and improve network resource utilization. The emergence of Software Defined Network (SDN) provides a more flexible and effective way to control traffic in the network. Existing TE solutions mainly focus on routing traffic via the shortest path or evenly distributing the traffic among multiple available paths, but these methods are not flexible since this static mapping of traffic to paths does not consider either the current network utilization or traffic load. Heuristics-based TE methods depends on operators' understanding of the workload and environment. Designing and implementing those methods thus take at least weeks. Furthermore, it usually takes minutes to output the solution. Inspired by recent successes in applying Deep Reinforcement Learning (DRL) techniques to solve complex control problems, we leverage DRL to control traffic in SDN. We start by building a framework which integrates the DRL algorithm into SDN. Based on this framework, we propose a modified DRL algorithm to control the traffic split ratio to multiple paths. Simulation results show that the proposed approach performs better than three baseline methods when the traffic load is dynamically changing.

One of the main control tasks in a computer network is to organize an effective system of information delivery; this task is of particular relevance in the software defined network. Conventional routing tools do not meet the requirements to service quality and the requirements for equitable distribution of congestion along communication channels. Routing in conventional networks is performed by the shortest path search based on a specified parameter, but these tools do not provide sufficient agility when changing routes in the network. Another drawback is the need to transmit regular updates of routing information by passing the service traffic, thereby dramatically increasing the congestion and reducing the throughput. At present, the most effective way to ensure the assigned quality of service parameters, as well as a promising solution to organize efficient routing under conditions of uncertainty, is a software defined network. This new networking paradigm makes it possible to simplify the process of managing the network, to significantly enhance the use of network resources, and to reduce operating costs. One of the main advantages of such a network is control at the upper levels of the reference model, which makes it possible to simplify both the process of network management and the process to manage traffic in corporate networks and data center networks. A new approach to traffic design in a software defined network has been proposed that employs the making-decision theory oriented towards routing exactly in such networks. If there is a «problematic area» and there is the need to overcome it, the decision-making theory under conditions of uncertainty is used, since the probability of selecting the best way to circumvent it accounts for the patterns in transmitted traffic. Such a method makes it possible to reduce the loss of inelastic traffic that is an important component of the overall amount of transmitted information. From a practical point of view, the algorithm constructed in this work, when compared to known algorithms for traffic engineering, improves the quality of service in software defined networks

Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header information as well as payload. Furthermore, to automatically respond to various applications, it is necessary to classify traffic using deep learning without the network operator intervention. In this study, we propose a traffic classification scheme using a deep learning model in software defined networks. We generate flow-based payload datasets through our own network traffic pre-processing, and train two deep learning models: 1) the multi-layer long short-term memory (LSTM) model and 2) the combination of convolutional neural network and single-layer LSTM models, to perform network traffic classification. We also execute a model tuning procedure to find the optimal hyper-parameters of the two deep learning models. Lastly, we analyze the network traffic classification performance on the basis of the F1-score for the two deep learning models, and show the superiority of the multi-layer LSTM model for network packet classification.

Software-Defined Network (SDN) represents a new network paradigm. Unlike conventional networks, SDNs separate control planes and data planes. The function of a data plane is enabled using switches, whereas that of a control plane is facilitated by a controller. The controller learns network topologies and makes traffic forwarding decisions. However, some serious vulnerabilities are gradually exposed in the topology management services of current SDN controller designs. These vulnerabilities mainly exist in host tracking and link discovery services. Attackers can exploit these weak points to poison the network topology information in SDN controllers. In this study, a novel solution is proposed to defend against topology poisoning attacks. By analyzing the existing topology attack principles and threat models, this work constructs legal conditions for host migration to detect host hijacking attacks. The checking of the Link Layer Discovery Protocol (LLDP) source and integrity is designed to defend against link fabrication attacks. A relay-type link fabrication attack detection method based on entropy is also designed. Results show that the proposed solution can effectively detect existing topological attacks and provide complete and comprehensive topological security protection.