A Survey on Software-Defined Networking

Abstract

Emerging mega-trends (e.g., mobile, social, cloud, and big data) in information and communication technologies (ICT) are commanding new challenges to future Internet, for which ubiquitous accessibility, high bandwidth, and dynamic management are crucial. However, traditional approaches based on manual configuration of proprietary devices are cumbersome and error-prone, and they cannot fully utilize the capability of physical network infrastructure. Recently, software-defined networking (SDN) has been touted as one of the most promising solutions for future Internet. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. As a result, SDN is positioned to provide more efficient configuration, better performance, and higher flexibility to accommodate innovative network designs. This paper surveys latest developments in this active research area of SDN. We first present a generally accepted definition for SDN with the aforementioned two characteristic features and potential benefits of SDN. We then dwell on its three-layer architecture, including an infrastructure layer, a control layer, and an application layer, and substantiate each layer with existing research efforts and its related research areas. We follow that with an overview of the de facto SDN implementation (i.e., OpenFlow). Finally, we conclude this survey paper with some suggested open research challenges.

Flow termination signaling in the centralized pre-congestion notification architecture

“…[7][8][9] ) in a sense that the control of the network is moved to a central node where all decision making is done. Bringing the (c)PCN functionalities considered in this paper into SDN will enrich SDN, but has to the best of our knowledge not been considered yet in literature.…”

“…However, the cPCN signaling architecture fits very well to the centralized nature of the control architecture of emerging Software Defined Networks (SDN, see e.g. [7][8][9] ) that (amongst others) takes care of flow routing in the data plane. Therefore, the outcome of our study also shows potential for enriching SDN with flow admission control and flow termination functionalities according to the cPCN approach.…”

Learning analytics for IoE based educational model using deep learning techniques: architecture, challenges and applications

“…These sensors produce a huge amount of data which travels from source to destination and vice versa using a wireless medium, therefore in order to secure this data, we propose to encrypt the data using Twofish Cryptographic technique Schneier et al, 1999). Furthermore to ensure optimal use of bandwidth and faster data transfer, the concept of Software Defined Networking (SDN) (Xia et al, 2015;Braun & Menth, 2014;Kreutz et al, 2015) has been incorporated in our approach. Finally, in order to perform better analytics and predictions using the data received from these sensors, deep learning techniques have been used.…”

“…The system controller is the brain of the system which is responsible for coordinating all the activities within the system which dynamically selects the most appropriate path for data transfer on the basis of the real time network traffic inputs. SDN controller is also capable of managing the network traffic, bandwidths and topologies in real time as and when required (Xia et al, 2015;Braun & Menth, 2014;Kreutz et al, 2015).…”

Software networks

“…In essence, the controller periodically collects data from the devices to fetch the network state and then acts on it by remotely configuring their forwarding process. By concentrating the intelligence at the controller instead of distributing it across numerous devices, the development of sophisticated network applications becomes feasible [12]. These applications may leverage standard interfaces provided by the controller to read the network state at a higher level of abstraction.…”