Orion: A Hybrid Hierarchical Control Plane of Software-Defined Networking for Large-Scale Networks

2014 ◽  
Author(s):  
Yonghong Fu ◽  
Jun Bi ◽  
Kai Gao ◽  
Ze Chen ◽  
Jianping Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Hierarchical Control ◽  
Software Defined Networking ◽  
Control Plane ◽  
Large Scale Networks

scholarly journals Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

2020 ◽  
Vol 12 (9) ◽  
pp. 147 ◽  
Author(s):  
Babangida Isyaku ◽  
Mohd Soperi Mohd Zahid ◽  
Maznah Bte Kamat ◽  
Kamalrulnizam Abu Bakar ◽  
Fuad A. Ghaleb
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Software Defined Networking ◽  
Cloud Services ◽  
Future Research ◽  
Control Plane ◽  
Processing Load ◽  
Flow Table ◽  
Data Plane ◽  
Large Scale Networks

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

scholarly journals NVP: A Network Virtualization Proxy for Software Defined Networking

2016 ◽  
Vol 11 (5) ◽  
pp. 697 ◽  
Author(s):  
Billy Pinheiro ◽  
Eduardo Cerqueira ◽  
Antonio Abelem
Keyword(s):  
Large Scale ◽  
Network Virtualization ◽  
Software Defined Networking ◽  
Network Function Virtualization ◽  
Load Testing ◽  
Proof Of Concept ◽  
Network Resources ◽  
Network Function ◽  
Large Scale Networks ◽  
Proper Solutions

The combination of Network Function Virtualization (NFV) and Software Defined Networking (SDN) can improve the control and utilization of network resources. However, this issue still requires proper solutions to virtualize large-scale networks, which would allow the use of SDN and Virtualization in real environments.Thus, this paper proposes a virtualization architecture for SDN that relies on a proxy-based approach. The NVP (Network Virtualization Proxy) is a virtualization proxy that intercepts messages exchanged between controllers and switches SDN enabling network virtualization. An implementation of the proposal was developed as a proof of concept and load testing was performed showing that the solution can provide network virtualization in a scalable manner, using less than 2.5 MB of memory to manage 100 switches performing simultaneous requests, whereas FlowVisor requires more than 200 MB.

scholarly journals Intelligent and Resizable Control Plane for Software Defined Vehicular Network : A Deep Reinforcement Learning Approach

2021 ◽  
Author(s):  
karima Smida ◽  
Hajer Tounsi ◽  
Mounir Frikha
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Large Scale ◽  
Vehicular Network ◽  
Optimal Number ◽  
Traffic Load ◽  
Control Plane ◽  
Distributed Approach ◽  
Large Scale Networks ◽  
Application Requirements

Abstract Software-Defined Networking (SDN) has become one of the most promising paradigms to manage large scale networks. Distributing the SDN Control proved its performance in terms of resiliency and scalability. However, the choice of the number of controllers to use remains problematic. A large number of controllers may be oversized inducing an overhead in the investment cost and the synchronization cost in terms of delay and traffic load. However, a small number of controllers may be insufficient to achieve the objective of the distributed approach. So, the number of used controllers should be tuned in function of the traffic charge and application requirements. In this paper, we present an Intelligent and Resizable Control Plane for Software Defined Vehicular Network architecture (IRCP-SDVN), where SDN capabilities coupled with Deep Reinforcement Learning (DRL) allow achieving better QoS for Vehicular Applications. Interacting with SDVN, DRL agent decides the optimal number of distributed controllers to deploy according to the network environment (number of vehicles, load, speed etc.). To the best of our knowledge, this is the first work that adjusts the number of controllers by learning from the vehicular environment dynamicity. Experimental results proved that our proposed system outperforms static distributed SDVN architecture in terms of end-to-end delay and packet loss.

A Hybrid Hierarchical Control Plane for Flow-Based Large-Scale Software-Defined Networks

2015 ◽  
Vol 12 (2) ◽  
pp. 117-131 ◽  
Author(s):  
Yonghong Fu ◽  
Jun Bi ◽  
Ze Chen ◽  
Kai Gao ◽  
Baobao Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Hierarchical Control ◽  
Software Defined Networks ◽  
Control Plane

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

Sign in / Sign up

Export Citation Format

Share Document