An Improved Algorithm to Enhance the Performance of FAST TCP Congestion Control for Personalized Healthcare Systems

The development of personalized medical systems should be supported by a fast and stable network system. The FAST TCP network system is the appropriate support system for this purpose. However, when the FAST TCP is deployed, the static mapping selection method for protocol parameters is unable to guarantee the small queuing delay and fast convergence of the network simultaneously. By conducting theoretical analysis and simulation experiments, the relationships among FAST TCP protocol slow start condition, control law gain parameters, and FAST TCP system convergence rate were examined. To ensure the stability of the FAST TCP system and to select the smallest protocol parameters, an improved method to effectively accelerate the convergence velocity of the FAST TCP system is proposed in this study. In this method, the number of packets for staying in the buffer for FAST TCP connections was taken as the criterion of the slow start, and the gain parameter of the control law was dynamically adjusted according to the local information of each FAST TCP connection. Using this improved method, the FAST TCP system can achieve a stable and small queuing delay, whilst the FAST TCP system could converge quickly to the equilibrium point simultaneously.

Proportional-integral genetic algorithm controller for stability of TCP network

The life development and increase the number of internet users imposed an increase in data circulating on the internet network and then make the network more congestion. As a result of all this, some problems arose such as time delay in packets delivery, loss of packets, and exceed the buffer capacity for the middle routers. To overcome those problems, transmission control protocol and active queue management (TCP/AQM) have been used. AQM is the main approach used to control congestion and overcome those problems to improve network performance. This work proposes to use the proportional-integral (PI) controller with a genetic algorithm (GA) as an active queue manager for routers of the Internet. The simulation results show a good performance for managing the congestion with using proportional-integral genetic algorithm (GA-PI) controller better than the PI controller.

Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers

The delay in delivering packets and packets loss in computer networks is due to the problem of congestion in TCP/AQM routers, and to solve this crisis the TCP and AQM worked together. Where TCP provides secure data transfer and designed to handle congestion after its occurrence. While AQM predicts congestion and tries to resolve the problem before it occurs. In this work, the Fuzzy-PID (FPID) controller presented to manage the avoidance of congestion problems associated with TCP networks. The approach is based on hybridization between the Fuzzy Logic Controller (FLC) and the Proportional-Integral-Derivative(PID) controller, with the structure of Fuzzy-Set-Point-Wight(FSW) for PID controller that optimized by Antlion Optimizer(ALO) Using a linearized TCP congestion model. our target is to control the queue length of the router for the queue level in demand. The FPID controller shows good robustness for a Different scenario of the TCP network and the queue length response showed fast tracking capability with good robustness to network parameters changing in comparison to PID controller, all simulations are carried out using MATLAB 2017b.

Particle Swarm Optimization Based LQ-Servo Controller for Congestion Avoidance

The network congestion is an essential problem that leads to packets losing and performance degradation. Thus, preventing congestion in the network is very important to enhance and improve the quality of service. Active queue management (AQM) is the solution to control congestion in TCP network middle nodes to improve theire performance. We design a linear quadratic (LQ)-servo controller as an AQM applied to TCP network to control congestion and attempt to achieve high quality of service under dynamic network environments. The LQ-servo controller is proposed to provide queue length stabilization with a small delay and faster settling time. The designed controller parameters are tuned by using the particle swarm optimization (PSO) method. The PSO algorithm was fundamentally applied to find the optimal controller parameters Q and R, such that a good output response could be obtained. The PI controller is examined for comparison reasons. The MATLAB simulation result shows that the controller is more effective than the PI in reaching zero steadystate error with better congestion avoidance under the dynamic network environment. Moreover, the proposed controller achieves a smaller delay and faster settling time

Network Manipulation Using Network Scanning in SDN

Network scanning commonly implies the use of the computer network to collect information about the target systems. This type of scanning is performed by hackers for attacking the target and also by the system administrators for assessment of security and maintaining the system. Network scanning mainly analyzes the UDP and TCP network services that are running on the target, the operating system that is used by the target, and the security systems that are placed between the user and targeted hosts. Network scanning includes both the network port scanning and vulnerability scanning. Network manipulation is an effort that is made by the user to modify the network or structure of a network and thus using online network tools to achieve the target. Software-defined networking is a term that comprises several network technologies with the aim of making it adapt the features of flexibility. Key terms for SDN implementation include separation of functionality, virtualization in the network, and configuring programmatically. This chapter explores network manipulation using network scanning in SDN.