Enterprise tenants access networks with committed bandwidth quotas shared among multiple departments and diverse services within each department. As a result, cloud vendors need to simultaneously fulfill two requirements, i.e., committed bandwidth guarantee and tenant-specified service bandwidth allocation. Hierarchical bandwidth allocation is a widely used technology that satisfies both requirements. In traditional schemes, each tenant's traffic is processed by a single node, potentially leading to single-node failures. Previous works have enhanced reliability by extending existing schemes to distributed systems with tenant-level bandwidth allocation, but fail to meet both requirements simultaneously. To bridge this gap, we propose S-DAL, which can achieve both requirements through servicelevel distributed hierarchical bandwidth allocation. We introduce an efficient fluid model-based algorithm for bandwidth allocation and employ a memory utilization based flow rate estimation mechanism to deliver accurate flow rate measurements. Additionally, we integrate a burst detection to mitigate excessive packet loss caused by burst traffic. Through testbeds and simulations, we demonstrate that S-DAL effectively ensures tenant-specified service bandwidth allocation while only reducing the shortfall in committed bandwidth to less than 0.23%.

In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus. Our approach is formulated using the hierarchical Pitman-Yor process mixture model with the inverted Beta-Liouville (IBL) distribution as its component density, which has shown superior performance in modeling text data than the widely used Gaussian distribution. Moreover, we theoretically develop a convergence-guaranteed online learning algorithm that can effectively learn the proposed TDT model from a stream of news stories based on varational Bayes. The merits of our TDT approach are illustrated by comparing it with other well-defined clustering-based TDT approaches on different news data sets.