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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Can Graph Learning Improve Planning in LLM-based Agents? 38

Can Graph Learning Improve Planning in LLM-based Agents?

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Xixi Shen, Yifei Shan, Caihua Song, Kaitao Wang, Siwei Zhang, Bohang Feng, Jiarui Cheng, Hong Chen, Wei Xiong, Yun Li, Dongsheng Fudan University China Microsoft Research Asia China The Chinese University of Hong Kong Hong Kong Peking University China Washington University Saint Louis United States Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural language into solvable sub-tasks, thereby fulfilling the original requests. In this context, the sub-tasks can be naturally viewed as a graph, where the nodes represent the sub-tasks, and the edges denote the dependencies among them. Consequently, task planning is a decision-making problem that involves selecting a connected path or subgraph within the corresponding graph and invoking it. In this paper, we explore graph learning-based methods for task planning, a direction that is orthogonal to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs, which is adeptly addressed by graph neural networks (GNNs). This theoretical insight led us to integrate GNNs with LLMs to enhance overall performance. Extensive experiments demonstrate that GNN-based methods surpass existing solutions even without training, and minimal training can further enhance their performance. The performance gain increases with a larger task graph size. © 2024 Neural information processing systems foundation. All rights reserved.

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A Method of Constructing and Automatically Labeling Radio Frequency Signal Training dataset for UAV 48

A Method of Constructing and Automatically Labeling Radio Fr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Chao Ma, Ruipeng Si, Zheng Chi, Mingmin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Zhengzhou University School of Cyber Science and Engineering China Zhengzhou University Henan Institute of Advanced Technology China

ISBN: (纸本)9781728163277

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%. © 2023 IEEE.

关键词： Object detection

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Enhancing Code Generation Through Retrieval of Cross-Lingual Semantic Graphs

Enhancing Code Generation Through Retrieval of Cross-Lingual...

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Asia-Pacific Conference on Software Engineering

作者： Zhijie Jiang Zejian Shi Xinyu Gao Yun Xiong School of Computer National University of Defense Technology China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

In the field of software engineering automation, code language models have made significant strides in code generation tasks. However, due to the cost of updating knowledge and the issue of hallucinations, code language models (CLMs) face challenges in practical code generation scenarios, making retrieval-augmented code generation a mainstream approach. Existing retrieval-augmented methods only build codebases for a single programming language, which is insufficient to address the lack of monolingual knowledge. To address this, we propose CodeRCSG, a novel cross-lingual retrieval-augmented code generation method. This method constructs a multilingual codebase and creates a unified cross-lingual code semantic graph to capture deep semantic information across different programming languages. By encoding the retrieved code semantic graph with GNN and combining it with input text embeddings, code language models can effectively utilize the transferred cross-lingual programming knowledge to improve the quality of generated code. Experimental results show that CodeRCSG can significantly enhance the code generation capabilities of code language models.

关键词： Knowledge engineering computer languages Codes Costs Semantics Retrieval augmented generation Programming Encoding Multilingual Software engineering

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

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Frontiers of computer science 2024年第6期18卷 215-227页

作者： Xiaochuan LIN Kaimin WEI Zhetao LI Jinpeng CHEN Tingrui PEI College of Information Science and Technology&Cyberspace Security Jinan UniversityGuangzhou 510632China National&Local Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou 510632China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.

关键词： task allocation aggregation shortest path dual heterogeneous spatial crowdsourcing

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Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

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QUILL: Quotation Generation Enhancement of Large Language Models

arXiv

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arXiv 2024年

作者： Xiao, Jin Zhang, Bowei He, Qianyu Liang, Jiaqing Wei, Feng Chen, Jinglei Liang, Zujie Yang, Deqing Xiao, Yanghua School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China MYbank Ant Group China

While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing factual quotations or fail to provide quotes that exceed human expectations. To bridge the gap, we systematically study how to evaluate and improve LLMs’ performance in quotation generation tasks. We first establish a holistic and automatic evaluation system for quotation generation task, which consists of five criteria each with corresponding automatic metric. To improve the LLMs’ quotation generation abilities, we construct a bilingual knowledge base that is broad in scope and rich in dimensions, containing up to 32,022 quotes. Moreover, guided by our critiria, we further design a quotation-specific metric to rerank the retrieved quotations from the knowledge base. Extensive experiments show that our metrics strongly correlate with human preferences. Existing LLMs struggle to generate desired quotes, but our quotation knowledge base and reranking metric help narrow this gap. Our dataset and code are publicly available at https://***/GraceXiaoo/QUILL. © 2024, CC BY.

关键词： Modeling languages

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Layer normalized OpenGAN framework for human intrusion activity recognition in a φ-OTDR optical fiber sensing system

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Optics Express 2025年第10期33卷 20272-20295页

作者： Zhou, Zhengxian Wang, Xiankun Xie, Tianchang Qu, Jun Shi, Jianping Zhang, Dawei Peng, Jiansheng Zhuang, Songlin Anhui Province Key Laboratory for Control and Applications of Optoelectronic Information Materials Anhui Normal University Wuhu241000 China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Artificial Intelligence and Manufacturing Hechi University Guangxi546300 China

Distributed optical fiber vibration sensing systems (DVS) are widely employed in perimeter security for their high sensitivity, simplicity, and strong immunity to electromagnetic interference. However, these systems are facing with two serious challenges: accurately classifying closed-set signals (known events) and detecting open-set signals (unknown events). To address this, we propose an open-set recognition framework, ResEff-OpenGAN-LN. By integrating layer normalization into the OpenGAN architecture, this framework mitigates instability caused by input feature variations while leveraging ResEff for efficient feature extraction to enhance closed-set classification. Experimental results show that ResEff achieves 99.92% accuracy on closed-set tasks, and ResEff-OpenGAN-LN obtains an AUROC of 0.9900 with 96.63% overall accuracy on mixed datasets containing open-set and closed-set signals, validating its potential to improve intrusion detection and reduce false alarms. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Light interference

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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

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Journal of Systems science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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