Abstract
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.
Highlights
With the continuous advancement of the digital wave, more and more hospitals are actively rolling out personalized healthcare systems with the goal of achieving rapid development in the field of intelligent medical care
The main contribution of this paper is to overcome the above defects; in the case of ensuring FAST TCP system stability, the FAST TCP flow chooses the protocol parameters as small as possible, adaptive dynamic adjustment slow start threshold and gain parameters according to the local information source end
The number of remaining link buffers was taken as the judgment condition of a slow start
Summary
With the continuous advancement of the digital wave, more and more hospitals are actively rolling out personalized healthcare systems with the goal of achieving rapid development in the field of intelligent medical care. Literature [13] used measurement facilities distributed in the network to monitor the backbone link state and applied fuzzy control technology to guide the source end of each FAST TCP connection to select appropriate protocol parameters according to the link state to stabilise queue delay; their proposed system did not improve the convergence speed of FAST TCP. The main contribution of this paper is to overcome the above defects; in the case of ensuring FAST TCP system stability, the FAST TCP flow chooses the protocol parameters as small as possible, adaptive dynamic adjustment slow start threshold and gain parameters according to the local information source end This improved method can ensure that the system is stable and has small queuing delay at the same time, improving the speed of convergence to equilibrium system.
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