Extending Stochastic Network Calculus to Loss Analysis

Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.

Deterministic Bounds on Performance of Self-Similar Network Based on ETS

According to the traffic regulating method and the traffic scheduling method of QoS control methods, this paper regulates traffic using the fractal regulator and proposes the arrival curve according to the output envelope of the fractal regulator. In addition, we put forward the service curve based on Enhanced Transmission Selection (ETS). Finally, we drive the deterministic bounds on performance of the self-similar network using the arrival curve and the service curve based on network calculus. These bounds have reference value for QoS guarantee, admission control and network planning.