A Temporal Network Calculus for Performance Analysis of Computer Networks

One inevitable trend of network development is to deliver information with various traffic characteristics and diverse Quality of Service (QoS) requirements. In response to the continually growing demand for more bandwidth, network performance analysis is needed to optimize the performance of existing technologies and evaluate the efficiency of new ones. Performance analysis investigates how traffic management mechanisms deployed in the network affect the resource allocation among users and the performance which the users experience. This topic can be investigated by constructing models of traffic management mechanisms and studying how these mechanisms perform under various types of network traffic. To this end, appropriate mathematical models are needed to characterize the traffic management mechanisms which we are interested in and represent different types of network traffic. In addition, fundamental properties which can be employed to manipulate the models should be explored. Over the last two decades a relatively new theory, stochastic network calculus, has been developed to enable mathematical performance analysis of computer networks. Particularly, several related processes are mathematically modeled, including the arrival process, the waiting process and the service process. This theory can be applied to the derivation and calculation of several performance metrics such as the backlog bound and the delay bound. The most attractive contribution of stochastic network calculus is to characterize the behavior of a process based on some bound on the complementary cumulative distribution function (CCDF). The behavior of a computer network is often subject to many irregularities and stochastic fluctuations. The models based on the bound on the CCDF are not very accurate, while they are more feasible for abstracting computer network systems and