On Achieving Proportional Loss Differentiation Using Dynamic-MQDDP with Differential Drop Probability
More Recently, researchers have explored to provide a queue management scheme with differentiated loss guarantees for the future Internet. Various types of real time and non-real time traffic with varying requirements are transmitted over the Internet. The sides of a packet drop rate, an each class to differential drop probability on achieving a low delay and high traffic intensity. Improved a queue management scheme to be enhanced to offer a drop probability is desired necessarily. This paper considers multiple random early detection with differential drop probability which is a slightly modified version of the MQDDP model, to get the performance of the best suited, we analyzes its main control parameters (max th , min th , max p ) for achieving the proportional loss differentiation (PLD) model, and gives their setting guidance from the analytic approach. we propose Dynamic-multiple queue management scheme based on differential drop probability, called Dynamic-MQDDP, is proposed to overcome MQDDP’s shortcoming as well as supports static max p parameter setting values for relative and each class proportional loss differentiation. MQDDP is static according to the situation of the network traffic, Network environment is very dynamic situation. Therefore max p parameter values needs to modify too to the constantly and dynamic. The verification of the guidance is shown with figuring out loss probability using a proposed algorithm under dynamic offered load and is also selection problem of optimal values of parameters for high traffic intensity and show that Dynamic-MQDDP has the better performance in terms of packet drop rate. We also demonstrated using an ns-2 network simulation.
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