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%.

Entropy search and its derivative methods are one class of Bayesian Optimization methods that achieve active exploration of black-box functions. They maximize the information gain about the position in the input space where the black-box function gets the global optimum. However, existing entropy search methods suffer from harassment caused by high dimensional optimization problems. On the one hand, the computation for estimating entropies increases exponentially as dimensions increase, which limits the applicability of entropy search to high dimensional problems. On the other hand, many high-dimensional problems have the property that a large number of dimensions have little influence on the objective function, but currently there is no compress mechanism to exclude these redundant dimensions. In this work, we propose Active Compact Entropy Search (AcCES) to fix these defects. Under the guidance of historical evaluation, AcCES actively explores the prevalent inter-dimensional correlations by maximizing the linear or non-linear relationships that may exist between dimensions in the acquisition function, which is ignored by existing Bayesian Optimization methods. In order to build a more compact input space, redundant dimensions are compressed by exploiting inter-dimensional correlations. Experiments demonstrate that AcCES achieves higher query efficiency and optimal results than existing entropy search methods.

Semi-supervised domain adaptation (SSDA) is a novel branch of machine learning that scarce labeled target examples are available, compared with unsupervised domain adaptation. To make effective use of these additional data so as to bridge the domain gap, one possible way is to generate adversarial examples, which are images with additional perturbations, between the two domains and fill the domain gap. Adversarial training has been proven to be a powerful method for this purpose. However, the traditional adversarial training adds noises in arbitrary directions, which is inefficient to migrate between domains, or generate directional noises from the source to target domain and reverse. In this work, we devise a general bidirectional adversarial training method and employ gradient to guide adversarial examples across the domain gap, i.e., the Adaptive Adversarial Training (AAT) for source to target domain and Entropy-penalized Virtual Adversarial Training (E-VAT) for target to source domain. Particularly, we devise a Bidirectional Adversarial Training (BiAT) network to perform diverse adversarial trainings jointly. We evaluate the effectiveness of BiAT on three benchmark datasets and experimental results demonstrate the proposed method achieves the state-of-the-art.