arXiv subjects

Chenhao Guo

Publications and source records attributed to Chenhao Guo.

4 recordsLinked to original sources

arXiv2026

IORM: Hierarchical I/O Governance for Thousands of Consolidated Databases on Oracle Exadata

Oracle Exadata consolidates thousands of tenant databases onto shared storage infrastructure deployed at hundreds of customer sites worldwide. Oracle Multitenant architecture enables this extreme density, with thousands of tenant databases sharing a single Exadata storage system -- but this creates a multi-level resource hierarchy (container databases, tenant databases, and workloads within tenants) that commodity block-layer schedulers cannot govern, as they lack visibility into database semantics and tenant boundaries. This paper presents the I/O Resource Manager (IORM), a storage-side scheduler built on three mechanisms: I/O Tagging, which propagates semantic context from the database kernel to the storage scheduler; Hierarchical Resource Profiles, which express compositional allocation policies across consolidation tiers using shares and limits; and Unified Storage Governance, which applies these policies consistently across all tiers of the storage hierarchy -- persistent memory, flash, and hard disk -- including cache placement decisions. IORM enables successful cloud deployments where thousands of tenants coexist on shared storage: production OLTP workloads run alongside concurrent analytical workloads from the same or different databases without noisy-neighbor interference. Evaluation on production Exadata systems demonstrates that IORM dramatically improves latency consistency, virtually eliminating tail latency outliers and delivering several-fold improvements in average read latency under mixed workloads. Hierarchical limits compose correctly across all three levels, and proportional share allocation tracks configured ratios closely even under highly skewed demand.

cs.DB↗

arXiv2025

An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks

Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's prior knowledge of the sensitive attributes and realize membership inference attacks (MIA) by observing and analyzing the topology distribution. As privacy concerns grow, the assumption of MIA, which presumes that attackers can obtain an auxiliary dataset with the same distribution, is increasingly deviating from reality. In this paper, we categorize the distribution diversity issue in real-world MIA scenarios as an Out-Of-Distribution (OOD) problem, and propose a novel Graph OOD Membership Inference Attack (GOOD-MIA) to achieve cross-domain graph attacks. Specifically, we construct shadow subgraphs with distributions from different domains to model the diversity of real-world data. We then explore the stable node representations that remain unchanged under external influences and consider eliminating redundant information from confounding environments and extracting task-relevant key information to more clearly distinguish between the characteristics of training data and unseen data. This OOD-based design makes cross-domain graph attacks possible. Finally, we perform risk extrapolation to optimize the attack's domain adaptability during attack inference to generalize the attack to other domains. Experimental results demonstrate that GOOD-MIA achieves superior attack performance in datasets designed for multiple domains.

cs.LG↗

arXiv2023

U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion

In image fusion tasks, images obtained from different sources exhibit distinct properties. Consequently, treating them uniformly with a single-branch network can lead to inadequate feature extraction. Additionally, numerous works have demonstrated that multi-scaled networks capture information more sufficiently than single-scaled models in pixel-level computer vision problems. Considering these factors, we propose U2Net, a spatial-spectral-integrated double U-shape network for image fusion. The U2Net utilizes a spatial U-Net and a spectral U-Net to extract spatial details and spectral characteristics, which allows for the discriminative and hierarchical learning of features from diverse images. In contrast to most previous works that merely employ concatenation to merge spatial and spectral information, this paper introduces a novel spatial-spectral integration structure called S2Block, which combines feature maps from different sources in a logical and effective way. We conduct a series of experiments on two image fusion tasks, including remote sensing pansharpening and hyperspectral image super-resolution (HISR). The U2Net outperforms representative state-of-the-art (SOTA) approaches in both quantitative and qualitative evaluations, demonstrating the superiority of our method. The code is available at https://github.com/PSRben/U2Net.

eess.IV↗

arXiv2020

Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings

Interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDI) is one of the key tasks in public health and drug development. Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new knowledge graph embedding framework by introducing adversarial autoencoders (AAE) based on Wasserstein distances and Gumbel-Softmax relaxation for drug-drug interactions tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation--an inherent flaw in traditional generative models--we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks, link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines.

cs.LG↗