Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> This paper presents Ethane, a new network architecture for the enterprise. Ethane allows managers to define a single network-wide fine-grain policy, and then enforces it directly. Ethane couples extremely simple flow-based Ethernet switches with a centralized controller that manages the admittance and routing of flows. While radical, this design is backwards-compatible with existing hosts and switches. We have implemented Ethane in both hardware and software, supporting both wired and wireless hosts. Our operational Ethane network has supported over 300 hosts for the past four months in a large university network, and this deployment experience has significantly affected Ethane's design. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Monitoring transit traffic at one or more points in a network is of interest to network operators for reasons of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Previous research in the area has focused on deriving a placement of monitors across the network toward the end of maximizing the monitoring utility of the network operator for a given traffic routing. However, both traffic characteristics and measurement objectives can dynamically change over time, rendering a previously optimal placement of monitors suboptimal. It is not feasible to dynamically redeploy/reconfigure measurement infrastructure to cater to such evolving measurement requirements. We address this problem by strategically routing traffic subpopulations over fixed monitors. We refer to this approach as MeasuRouting. The main challenge for MeasuRouting is to work within the constraints of existing intradomain traffic engineering operations that are geared for efficiently utilizing bandwidth resources, or meeting quality-of-service (QoS) constraints, or both. A fundamental feature of intradomain routing, which makes MeasuRouting feasible, is that intradomain routing is often specified for aggregate flows. MeasuRouting can therefore differentially route components of an aggregate flow while ensuring that the aggregate placement is compliant to original traffic engineering objectives. In this paper, we present a theoretical framework for MeasuRouting. Furthermore, as proofs of concept, we present synthetic and practical monitoring applications to showcase the utility enhancement achieved with MeasuRouting. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Network virtualization is the key to the current and future success of cloud computing. In this article, we explain key reasons for virtualization and briefly explain several of the networking technologies that have been developed recently or are being developed in various standards bodies. In particular, we explain software defined networking, which is the key to network programmability. We also illustrate SDN?s applicability with our own research on OpenADN - application delivery in a multi-cloud environment. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> We present the design, implementation, and evaluation of B4, a private WAN connecting Google's data centers across the planet. B4 has a number of unique characteristics: i) massive bandwidth requirements deployed to a modest number of sites, ii) elastic traffic demand that seeks to maximize average bandwidth, and iii) full control over the edge servers and network, which enables rate limiting and demand measurement at the edge. These characteristics led to a Software Defined Networking architecture using OpenFlow to control relatively simple switches built from merchant silicon. B4's centralized traffic engineering service drives links to near 100% utilization, while splitting application flows among multiple paths to balance capacity against application priority/demands. We describe experience with three years of B4 production deployment, lessons learned, and areas for future work. <s> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> The oversubscription of intra-Data Center links and the high volatility of Virtual Machine deployments require a flexible and agile control of the Data Center network infrastructure, possibly coordinated with computing and storage resources control. In this scenario, the Software-Defined Networking paradigm opens up new opportunities to design orchestrated resources control strategies able to meet the dynamically-changing traffic demands of instantiated Virtual Machines. This paper presents the architectural design of an SDN-based orchestrator for dynamic computing and communication resources chaining, able to assure better than best effort VMs data exchanges across Cloud Data Centers. Moreover, the paper evaluates a number of different chaining strategies able to dynamically compose proper pool of resources based on their variable load. Such strategies are compared against specific management objectives using both simulations and experiments showing their effectiveness in terms of resource usage and service provisioning rate. <s> BIB005 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Software Defined Networking (SDN) is an emerging networking paradigm that separates the network control plane from the data forwarding plane with the promise to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution. Although traffic engineering techniques have been widely exploited in the past and current data networks, such as ATM networks and IP/MPLS networks, to optimize the performance of communication networks by dynamically analyzing, predicting, and regulating the behavior of the transmitted data, the unique features of SDN require new traffic engineering techniques that exploit the global network view, status, and flow patterns/characteristics available for better traffic control and management. This paper surveys the state-of-the-art in traffic engineering for SDNs, and mainly focuses on four thrusts including flow management, fault tolerance, topology update, and traffic analysis/characterization. In addition, some existing and representative traffic engineering tools from both industry and academia are explained. Moreover, open research issues for the realization of SDN traffic engineering solutions are discussed in detail. <s> BIB006 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Software Defined Networking (SDN) is an exciting technology that enables innovation in how we design and manage networks. Although this technology seems to have appeared suddenly, SDN is part of a long history of efforts to make computer networks more programmable. In this paper, we trace the intellectual history of programmable networks, including active networks, early efforts to separate the control and data plane, and more recent work on OpenFlow and network operating systems. We highlight key concepts, as well as the technology pushes and application pulls that spurred each innovation. Along the way, we debunk common myths and misconceptions about the technologies and clarify the relationship between SDN and related technologies such as network virtualization. <s> BIB007 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> The idea of programmable networks has recently re-gained considerable momentum due to the emergence of the Software-Defined Networking (SDN) paradigm. SDN, often referred to as a "radical new idea in networking", promises to dramatically simplify network management and enable innovation through network programmability. This paper surveys the state-of-the-art in programmable networks with an emphasis on SDN. We provide a historic perspective of programmable networks from early ideas to recent developments. Then we present the SDN architecture and the OpenFlow standard in particular, discuss current alternatives for implementation and testing of SDN-based protocols and services, examine current and future SDN applications, and explore promising research directions based on the SDN paradigm. <s> BIB008 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Realizing the benefits of SDN for many network management applications (e.g., traffic engineering, service chaining, topology reconfiguration) involves addressing complex optimizations that are central to these problems. Unfortunately, such optimization problems require (a) significant manual effort and expertise to express and (b) non-trivial computation and/or carefully crafted heuristics to solve. Our goal is to simplify the deployment of SDN applications using general high-level abstractions for capturing optimization requirements from which we can efficiently generate optimal solutions. To this end, we present SOL, a framework that demonstrates that it is possible to simultaneously achieve generality and efficiency. The insight underlying SOL is that many SDN applications can be recast within a unifying path-based optimization abstraction. Using this, SOL can efficiently generate near-optimal solutions and device configurations to implement them. We show that SOL provides comparable or better scalability than custom optimization solutions for diverse applications, allows a balancing of optimality and route churn per reconfiguration, and interfaces with modern SDN controllers. <s> BIB009 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Background/Objectives: This paper surveys the application of Artificial Intelligence (AI) to the Software Defined Networking (SDN) paradigm which is a part of previous efforts to give the computer networks the ability of being programmed based on the separation between the control and forwarding planes. In SDN approach, the controller represents the central brain of the network which leads to an advanced level of flexibility and network intelligence. Methods/Statistical Analysis: Different artificial intelligence-based techniques have been applied to achieve an enhanced load balance, network security and intelligent network applications in the SDN approach. Findings: Ant colony algorithms were successful in increasing the maximal Quality of Experience (QoE) by 24.1% compared with the shortest path routing approach. Neural network based intrusion prevention system has shown a scalable performance with low false positive rate. Applying reinforcement learning based technique in adaptive video streaming system compared with the shortest path routing and greedy-based approaches has shown decreasing of the frame loss rate by 89% and 70% respectively. Applications/Improvements: This study highlights the first attempts for applying artificial intelligence in SDN paradigm. However, hybrid intelligent techniques could be the key for achieving more advanced behaviour in SDN-based networks. <s> BIB010 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> INTRODUCTION <s> Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects— edge , access , core , and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies. <s> BIB011
Software-defined networking (SDN) adopts the concept of programmable networks by using a logically centralized management, which represents a simplified solution for complex tasks such as traffic engineering BIB002 , network optimization BIB009 and orchestration BIB005 . Furthermore, dealing with modern network applications requires more scalable architecture which should be able to provide reliable and sufficient services based on a specific traffic type BIB006 . This can be achieved with the SDN architecture, which maintains a global view of network states and provides a flow-level control of the underlying layers BIB006 . This idea caused a dramatical change in the way how networks are designed and managed BIB007 BIB008 . In addition, the SDN architecture allows the involvement of third parties in the design and deployment of modern network applications BIB008 . Ethane project BIB001 formed the foundation of today's SDN by presenting a new networking paradigm, in which a centralized controller is used for flow-level policy management and security purposes in enterprise networks. The SDN paradigm separates the control plane from the data plane, which results in achieving much faster and dynamic approach in compared with a conventional network architecture BIB003 . The control plane can be split into several virtual networks where each one implements a different policy BIB003 . As a result, this paradigm can be viewed as a tool allows addressing various issues in networking from another perspective BIB007 and can be used also to fulfil the requirements of new technologies such as internet of things (IoT) and 5G BIB011 . The adoption of SDN paradigm, however, strongly depends on its success in reaching an appropriate solution for the problems, which cannot be solved by the traditional networking protocols and architectures  . Some large companies such as Microsoft and Google have already started using the SDN paradigm for their own data centres BIB004  . Artificial Intelligence (AI), on the other hand, reveals a huge potential in SDN innovation. Our previous study BIB010 focused on highlighting the first efforts to integrate AI in SDN. In this work, however, we provide a thorough overview of the research efforts in this area to a gain deeper insight into the significant role of AI in SDN paradigm.