Simple solution for low cost bandwidth management

Organizations in this era can't be separated from information technology, especially in communication and information sharing. The existence of information technology, especially computer networks, greatly facilitates agencies in terms of communication. Organizations that have used computer networks generally don’t have tools to handle security and bandwidth management issues in large numbers, resulting in wasteful use of bandwidth for unproductive purposes, such as accessing video streaming. The fact is professional tools to overcome the problem of security and bandwidth management issues are already in the market, but have hundreds of millions of priced. The high price of professional devices gave an opportunity to develop a bandwidth management system based on the integration of the remote authentication dial in user service (RADIUS) server and Mikrotik RouterBoard, at a lower cost. RADIUS server was chosen as a service for network security, because it supports the legal authentication for users via AAA protocol. The RADIUS server can be integrated with MySQL database, it can be developed SSO systems. Bandwidth management can be done with Mikrotik feature, but has the disadvantages of scalable storage, that problem can overcome by integrating Mikrotik and RADIUS server, then defining time doing data packet quota for the client and its implementation can help with hypertext preprocessor (PHP) scripts

Cognitive Radio Platforms For Disaster Response Networks, Survey

Either natural or man-made a disaster is defined as unexpected destructive event that causes damages and malfunction of existing systems and services all around the disaster area, these destructive effects are unfortunately beyond the capability of local authorities to recover and respond immediately, the disaster recovery plans are immediately initiated so that rescue and aid operations can help those who are trapped in disaster area to survive, those efforts need to be controlled and coordinated with reliable communication systems that are more likely partially or fully disabled due to the disaster, the capabilities of cognitive radio technology enables it to play a significant role in providing efficient communication services for the rescue teams and headquarters as well as trapped victims, in this paper, we survey the cognitive radio architectures that can replace the Software Defined Radio SDR in order to reduce the network expenses in terms of network size and network computational complexit

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2 = 0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.This work was supported by AGH University of Science and Technology grant, under contract no. 15.11.230.400, the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE) and the Catalan Institution for Research and Advanced Studies (ICREA). The research was also supported in part by PL-Grid Infrastructure.Peer ReviewedPostprint (author's final draft

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0.86$) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.Comment: 12 page