On software-defined networking and the design of SDN controllers

© 2015 IEEE. Software-Defined Networking (SDN) has emerged as a networking paradigm that can remove the limitations of current network infrastructures by separating the control plane from the data forwarding plane. The implications include: the underlying network state and decision making capability are centralized; programmability is provided on the control plane; the operation at the forwarding plane is simplified; and the underlying network infrastructure is abstracted and presented to the applications. This paper discusses and exposes the details of the design of a common SDN controller based on our study of many controllers. The emphasis is on interfaces as they are essential for evolving the scope of SDN in supporting applications with different network resources requirements. In particular, the paper review and compare the design of the three controllers: Beacon, OpenDaylight, and Open Networking Operation System

Analisis dan Implementasi Software Defined Networking (SDN) untuk Automasi Perangkat Jaringan

This study aims to build a network automation infrastructure by implementing a Software Defined Networking (SDN)-based web application to facilitate network administrators in implementing a web-based controller system for network devices by optimizing the configuration, management and operation time of network devices. This research uses action research method. In the action research method, researchers can describe, interpret and explain a condition at the same time as carrying out interventions aimed at improvement or participation. The results of this study indicate that the design and implementation of a Software Defined Networking (SDN)-based web application for network device automation using the python programming language with the paramiko library and the django framework can be applied to network infrastructure, so that with the application of SDN on network device automation applications can perform controllers. centralized network devices, so that configurations of many routers can be optimally carried out on a single Software Defined Networking (SDN) based web application

The Role of Inter-Controller Traffic for Placement of Distributed SDN Controllers

We consider a distributed Software Defined Networking (SDN) architecture adopting a cluster of multiple controllers to improve network performance and reliability. Besides the Openflow control traffic exchanged between controllers and switches, we focus on the control traffic exchanged among the controllers in the cluster, needed to run coordination and consensus algorithms to keep the controllers synchronized. We estimate the effect of the inter-controller communications on the reaction time perceived by the switches depending on the data-ownership model adopted in the cluster. The model is accurately validated in an operational Software Defined WAN (SDWAN). We advocate a careful placement of the controllers, that should take into account both the above kinds of control traffic. We evaluate, for some real ISP network topologies, the delay tradeoffs for the controllers placement problem and we propose a novel evolutionary algorithm to find the corresponding Pareto frontier. Our work provides novel quantitative tools to optimize the planning and the design of the network supporting the control plane of SDN networks, especially when the network is very large and in-band control plane is adopted. We also show that for operational distributed controllers (e.g. OpenDaylight and ONOS), the location of the controller which acts as a leader in the consensus algorithm has a strong impact on the reactivity perceived by switches.Comment: 14 page

Disaster-Resilient Control Plane Design and Mapping in Software-Defined Networks

Communication networks, such as core optical networks, heavily depend on their physical infrastructure, and hence they are vulnerable to man-made disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction (WMD) attacks, as well as to natural disasters. Large-scale disasters may cause huge data loss and connectivity disruption in these networks. As our dependence on network services increases, the need for novel survivability methods to mitigate the effects of disasters on communication networks becomes a major concern. Software-Defined Networking (SDN), by centralizing control logic and separating it from physical equipment, facilitates network programmability and opens up new ways to design disaster-resilient networks. On the other hand, to fully exploit the potential of SDN, along with data-plane survivability, we also need to design the control plane to be resilient enough to survive network failures caused by disasters. Several distributed SDN controller architectures have been proposed to mitigate the risks of overload and failure, but they are optimized for limited faults without addressing the extent of large-scale disaster failures. For disaster resiliency of the control plane, we propose to design it as a virtual network, which can be solved using Virtual Network Mapping techniques. We select appropriate mapping of the controllers over the physical network such that the connectivity among the controllers (controller-to-controller) and between the switches to the controllers (switch-to-controllers) is not compromised by physical infrastructure failures caused by disasters. We formally model this disaster-aware control-plane design and mapping problem, and demonstrate a significant reduction in the disruption of controller-to-controller and switch-to-controller communication channels using our approach.Comment: 6 page

A Collaborative and Distributed Learning-Based Solution to Autonomously Plan Computer Networks

The high programmability provided by Software Defined Networking (SDN) paradigm facilitated the integration of Machine Learning (ML) methods to design a new family of network management schemes. Among them, we can cite self-driving networks, where ML is used to analyze data and define strategies that are then translated into network configurations by the SDN controllers, making the networks autonomous and capable of auto-scaling decisions based on the network’s needs. Despite their attractiveness, the centralized design of the majority of proposed solutions cannot keep up with the increasing size of the network. To this end, this paper investigates the use of a multiagent reinforcement learning (MARL) model for auto-scaling decisions in an SDN environment. In particular, we study two possible alternatives for distributing operations: a collaborative one, where controllers share the same observations, and an individual one, where controllers make decisions according to their own logic and share only some basic information, such as the network topology. After an experimental campaign performed both on Mininet and GENI, results showed that both approaches can guarantee high throughput while minimizing the set of active resources