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Research on resource scheduling method based on Kubernetes

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Research on resource scheduling method based on Kubernetes

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Haoyang Song Liming Zhou Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

With the continuous development of cloud computing, the demand for cloud services in all walks of life is increasing. The scale of cloud computing services based on container technology is constantly growing, and Kubernetes has been widely used as a mainstream tool for container orchestration. It supports functions such as automatic deployment and service discovery, dramatically simplifies the container deployment process, and improves the availability and reliability of applications in cloud environments. However, the default scheduling strategy of Kubernetes adopts the greedy idea of first-come-first-served when facing multi-pod tasks, and schedules Pods to working nodes one by one. This scheduling scheme will lead to unbalanced cluster load and low resource utilization. To solve this problem, this paper introduces the selection, crossover, and mutation ideas in genetic algorithms into the artificial fish school algorithm, improves the foraging behavior in the artificial fish school algorithm, and constructs a custom scheduling strategy based on the enhanced artificial fish school algorithm. Experiments have shown that compared with the default scheduling strategy of Kubernetes, the customized scheduling strategy in this paper can effectively improve the load balancing of the cluster and improve the utilization of cluster hardware resources in the scenario of multi-pod scheduling.

关键词： Cloud computing Schedules Clustering algorithms Containers Fish Load management Scheduling Hardware Fish schools Genetic algorithms

GraphSparseNet: a Novel Method for Large Scale Trafffic Flow Prediction

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arXiv 2025年

作者： Kong, Weiyang Wu, Kaiqi Zhang, Sen Liu, Yubao Sun Yat-Sen University Guangzhou China Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Traffic flow forecasting is a critical spatio-temporal data mining task with wide-ranging applications in intelligent route planning and dynamic traffic management. Recent advancements in deep learning, particularly through Graph Neural Networks (GNNs), have significantly enhanced the accuracy of these forecasts by capturing complex spatio-temporal dynamics. However, the scalability of GNNs remains a challenge due to their exponential growth in model complexity with increasing nodes in the graph. Existing methods to address this issue, including sparsification, decomposition, and kernel-based approaches, either do not fully resolve the complexity issue or risk compromising predictive accuracy. This paper introduces GraphSparseNet (GSNet), a novel framework designed to improve both the scalability and accuracy of GNN-based traffic forecasting models. GraphSparseNet is comprised of two core modules: the Feature Extractor and the Relational Compressor. These modules operate with linear time and space complexity, thereby reducing the overall computational complexity of the model to a linear scale. Our extensive experiments on multiple real-world datasets demonstrate that GraphSparseNet not only significantly reduces training time by 3.51x compared to state-of-the-art linear models but also maintains high predictive performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

Aerial Vision-and-Language Navigation with Grid-based View Selection and Map Construction

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arXiv 2025年

作者： Zhao, Ganlong Li, Guanbin Pan, Jia Yu, Yizhou The University of Hong Kong Hong Kong Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China Peng Cheng Laboratory China

Aerial Vision-and-Language Navigation (Aerial VLN) aims to obtain an unmanned aerial vehicle agent to navigate aerial 3D environments following human instruction. Compared to ground-based VLN, aerial VLN requires the agent to decide the next action in both horizontal and vertical directions based on the first-person view observations. Previous methods struggle to perform well due to the longer navigation path, more complicated 3D scenes, and the neglect of the interplay between vertical and horizontal actions. In this paper, we propose a novel grid-based view selection framework that formulates aerial VLN action prediction as a grid-based view selection task, incorporating vertical action prediction in a manner that accounts for the coupling with horizontal actions, thereby enabling effective altitude adjustments. We further introduce a grid-based bird’s eye view map for aerial space to fuse the visual information in the navigation history, provide contextual scene information, and mitigate the impact of obstacles. Finally, a cross-modal transformer is adopted to explicitly align the long navigation history with the instruction. We demonstrate the superiority of our method in extensive experiments. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

MULTIMODAL QUANTITATIVE LANGUAGE FOR GENERATIVE RECOMMENDATION

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arXiv 2025年

作者： Zhai, Jianyang Mai, Zi-Feng Wang, Chang-Dong Yang, Feidiao Zheng, Xiawu Li, Hui Tian, Yonghong Sun Yat-sen University China Pengcheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China Xiamen University China Peking University China

Generative recommendation has emerged as a promising paradigm aiming at directly generating the identifiers of the target candidates. Most existing methods attempt to leverage prior knowledge embedded in Pre-trained Language Models (PLMs) to improve the recommendation performance. However, they often fail to accommodate the differences between the general linguistic knowledge of PLMs and the specific needs of recommendation systems. Moreover, they rarely consider the complementary knowledge between the multimodal information of items, which represents the multi-faceted preferences of users. To facilitate efficient recommendation knowledge transfer, we propose a novel approach called Multimodal Quantitative Language for Generative Recommendation (MQL4GRec). Our key idea is to transform items from different domains and modalities into a unified language, which can serve as a bridge for transferring recommendation knowledge. Specifically, we first introduce quantitative translators to convert the text and image content of items from various domains into a new and concise language, known as quantitative language, with all items sharing the same vocabulary. Then, we design a series of quantitative language generation tasks to enrich quantitative language with semantic information and prior knowledge. Finally, we achieve the transfer of recommendation knowledge from different domains and modalities to the recommendation task through pre-training and fine-tuning. We evaluate the effectiveness of MQL4GRec through extensive experiments and comparisons with existing methods, achieving improvements over the baseline by 11.18%, 14.82%, and 7.95% on the NDCG metric across three different datasets, respectively. 1 © 2025, CC BY.

关键词： Knowledge transfer

An enhanced sparse subset selection model based on adaptive projection distance

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Evolutionary Intelligence 2025年第3期18卷 1-15页

作者： Feng, Feng Gao, Erwei Zhi, Xiaobin Zhao, Yichuan School of Science and Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China School of Science and Research Center for Intelligent Systems and Data Science Xi’an University of Posts and Telecommunications Xi’an710121 China Department of Mathematics and Statistics Georgia State University AtlantaGA30303 United States

Subset selection focuses on identifying representative samples from a large dataset to produce a data subset that can represent the main features of the original data and also reduce the data size in an effective way. Sparse representation theory puts emphasis on enhancing the data sparsity, while subset selection aims to reduce the data size. Sparse subset selection is an important branch of subset selection that incorporates sparse representation theory into the exploration of subset selection. Sparse modeling representative selection model has demonstrated outstanding performance in data classification and video summarization. Nonetheless, it suffers from the issue of poor typicality of the selected representatives. Although some recent works have addressed this problem to some extent by introducing the concept of clustering coding terms, most of the clustering coding terms constructed by these existing methods are implicit, and the model structure is complicated, inefficient, and difficult to be understood. To overcome these difficulties, we propose an adaptive projection distance-based sparse subset selection model. This model constructs a clustering coding item by adaptively calculating the projection distance between data. It can not only eliminate redundant and noisy features in the original data but also ensure that the selected representatives are more typical. To solve the proposed model, an efficient algorithm is developed by virtue of the alternating direction method of multipliers. Extensive experiments with comparative analysis demonstrate that the proposed model outperforms several state-of-the-art models in various tasks including subset selection, cluster analysis, classification, and face image representative selection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Taxonomies

A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models

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arXiv 2025年

作者： Huang, Zhongzhan Zhong, Shanshan Zhou, Pan Gao, Shanghua Zitnik, Marinka Lin, Liang Sun Yat-sen University China Singapore Management University Singapore Harvard University United States Guangdong Key Laboratory of Big Data Analysis and Processing and Peng Cheng Laboratory China

Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativity of multimodal LLMs, with a focus on suitable evaluation platforms and methodologies. First, we find the Oogiri game—a creativity-driven task requiring humor, associative thinking, and the ability to produce unexpected responses to text, images, or both. This game aligns well with the input-output structure of modern multimodal LLMs and benefits from a rich repository of high-quality, human-annotated creative responses, making it an ideal platform for studying LLM creativity. Next, beyond using the Oogiri game for standard evaluations like ranking and selection, we propose LoTbench, an interactive, causality-aware evaluation framework, to further address some intrinsic risks in standard evaluations, such as information leakage and limited interpretability. The proposed LoTbench not only quantifies LLM creativity more effectively but also visualizes the underlying creative thought processes. Our results show that while most LLMs exhibit constrained creativity, the performance gap between LLMs and humans is not insurmountable. Furthermore, we observe a strong correlation between results from the multimodal cognition benchmark MMMU and LoTbench, but only a weak connection with traditional creativity metrics. This suggests that LoTbench better aligns with human cognitive theories, highlighting cognition as a critical foundation in the early stages of creativity and enabling the bridging of diverse concepts. Project Page. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction

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arXiv 2025年

作者： He, Zijian Ning, Yuwei Qin, Yipeng Wang, Wangrun Yang, Sibei Lin, Liang Li, Guanbin Sun Yat-sen University China Cardiff University United Kingdom ShanghaiTech University China Guangdong Key Laboratory of Big Data Analysis and Processing China Peng Cheng Laboratory China

Virtual Try-On (VTON) is a transformative technology in e-commerce and fashion design, enabling realistic digital visualization of clothing on individuals. In this work, we propose VTON 360, a novel 3D VTON method that addresses the open challenge of achieving high-fidelity VTON that supports any-view rendering. Specifically, we leverage the equivalence between a 3D model and its rendered multi-view 2D images, and reformulate 3D VTON as an extension of 2D VTON that ensures 3D consistent results across multiple views. To achieve this, we extend 2D VTON models to include multi-view garments and clothing-agnostic human body images as input, and propose several novel techniques to enhance them, including: i) a pseudo-3D pose representation using normal maps derived from the SMPL-X 3D human model, ii) a multi-view spatial attention mechanism that models the correlations between features from different viewing angles, and iii) a multi-view CLIP embedding that enhances the garment CLIP features used in 2D VTON with camera information. Extensive experiments on large-scale real datasets and clothing images from e-commerce platforms demonstrate the effectiveness of our approach. Project page: https://***/ VTON360. Copyright © 2025, The Authors. All rights reserved.

关键词： Clothes

SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language Models Tackling Knowledge-based Reasoning Tasks

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arXiv 2025年

作者： Wan, Wentao Yang, Zhuojie Chen, Yongcan Luo, Chenglin Wang, Ruilin Cai, Kehao Kang, Nan Lin, Liang Wang, Keze School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China South China Normal University China

Deductive reasoning is a crucial logical capability that assists us in solving complex problems based on existing knowledge. Although augmented by Chain-of-Thought prompts, Large Language Models (LLMs) might not follow the correct reasoning paths. Enhancing the deductive reasoning abilities of LLMs, and leveraging their extensive built-in knowledge for various reasoning tasks, remains an open question. Attempting to mimic the human deductive reasoning paradigm, we propose a multi-stage Syllogistic-Reasoning Framework of Thought (SR-FoT) that enables LLMs to perform syllogistic deductive reasoning to handle complex knowledge-based reasoning tasks. Our SR-FoT begins by interpreting the question and then uses the interpretation and the original question to propose a suitable major premise. It proceeds by generating and answering minor premise questions in two stages to match the minor premises. Finally, it guides LLMs to use the previously generated major and minor premises to perform syllogistic deductive reasoning to derive the answer to the original question. Extensive and thorough experiments on knowledge-based reasoning tasks have demonstrated the effectiveness and advantages of our SR-FoT. Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge based systems

Graph-Enhanced Multi-Scale Contrastive Learning for Graph Anomaly Detection with Adaptive Diffusion Models

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IEEE Transactions on Big data 2025年

作者： Xiao, Chunjing Tong, Yao Lu, Jiahui Xue, Yuxia Tai, Wenxin Cheng, Zhangtao Zhou, Fan The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The University of Electronic Science and Technology of China Chengdu610054 China

Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and various contrastive strategies have been initiated. However, these methods might force two instances (e.g., node-level and subgraph-level representations) with different category labels to be consistent during model training, which can adversely impact the model robustness. Also, they extract node-level representations only based on node attributes, which are inadequate in reflecting the information of the structural anomaly. To tackle this problem, we present a Graph-enhanced multi-scale Contrastive Learning framework for Anomaly Detection, GCLAD. In this framework, we design a diffusion probabilistic model-based graph enhancement module to adaptively manipulate neighbors to generate enhanced graphs, which can efficiently enhance subgraph-level representations and alleviate the inconsistent problem. Further, we present a multi-scale contrastive module where we introduce meta-paths to exploit a few relevant neighbors to boost node-level representations, and build the multi-scale contrastive losses to promote anomaly detection performance. Experimental results demonstrate the superiority of GCLAD compared with state-of-the-art baselines. © 2015 IEEE. All rights reserved.

关键词： Contrastive Learning