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

To support Quality of Service (QoS) management on current Internet working with best effort, we propose a systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee. Both QoS metrics and contexts of a service are considered in a comprehensive manner in our approach, which consists of three sequential stages: context discretization, QoS qualitative diagnosis and QoS quantitative guarantee. Based on Fuzzy set, an automatic unwatched discretization algorithm for discretizing continuous numeric-value is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with the help of K2 Bayesian network (BN) structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. An ordering method is proposed to arrange orders for nodes involved in K2 algorithm. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. BN inference is referred to calculate the marginal on a QoS metric node given its tunable parent context nodes. Then, the QoS metric is guaranteed to a specific value a user demands with certain probability by tuning its causal contexts to suitable values suggested by BN inference, that is, QoS quantitative guarantee is reached by now. Simulations, on a peer-to-peer (P2P) network, about the above three sequential stages are discussed and our approach is validated to be soundable and effective. We also argue that our approach can be reached in a polynomial time complexity in practice.