A Simple Proof of Linear Scaling of End-to-End Probabilistic Bounds using Network Calculus (1110.1801v5)

Abstract: Statistical network calculus is the probabilistic extension of network calculus, which uses a simple envelope approach to describe arrival traffic and service available for the arrival traffic in a node. One of the key features of network calculus is the possibility to describe the service available in a network using a network service envelope constructed from the service envelopes of the individual nodes constituting the network. It have been shown that the end-to-end worst case performance measures computed using the network service envelope is bounded by $ {\cal O} (H) $, where $H$ is the number of nodes traversed by a flow. There have been many attempts to achieve a similar linear scaling for end-to-end probabilistic performance measures but with limited success. In this paper, we present a simple general proof of computing end-to-end probabilistic performance measures using network calculus that grow linearly in the number of nodes ($H$).