Context-aware end-to-end QoS diagnosis and quantitative guarantee based on Bayesian network

Abstract

To support quality of service (QoS) management on current Internet working with best effort, we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis, we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network (BN) structure learning. Context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts, and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee, those causal relationships are first modeled quantitatively by BN parameter learning. Then, the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values, that is, quantitative QoS guarantee is reached. Simulations with three sequential stages: context discretization, QoS diagnosis and quantitative QoS guarantee, on a peer-to-peer (P2P) network, are discussed and our approach is validated to be effective.