End-to-end available bandwidth as a random autocorrelated QoS-relevant time-series

Estimating the reliability of an end-to-end network path is critically important for applications that support remote real-time task execution. Available bandwidth defined as a minimum spare capacity of links constituting a network path is an important QoS characteristic of the path. In this work we demonstrate a new approach to modeling available bandwidth behavior from a time-series analysis prospective. In particular, we introduce a notion of crossing probability   – the probability that the available bandwidth drops below the QoS critical threshold for the period of time required for the real-time task execution. We estimate the “crossing probability” by an application of the ARCH2ARCH2 (AutoRegressive Conditional Heteroscedasticity) model to the available bandwidth behavior. We characterize the network path by model coefficients β0β0 and β1β1, which may be evaluated and updated dynamically. We use these coefficients to quickly output the “crossing probability” for arbitrary values of the threshold and length of the real-time task. The model was evaluated on real bandwidth measurements across multiple network paths.