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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

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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Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Fu, Liqun Su, Yuhan Hosseinalipour, Seyyedali Wang, Xianbin Hong, Yiguang School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering Western University ON Canada

Hierarchical Federated Learning (HFL) introduces intermediate aggregation layers, addressing the limitations of conventional Federated Learning (FL) in geographically dispersed environments with limited communication infrastructure. An application of HFL is in smart IoT systems, such as remote monitoring, disaster response, and battlefield operations, where cellular connectivity is often unreliable or unavailable. In these scenarios, UAVs serve as mobile aggregators, providing connectivity to the terrestrial IoT devices. This paper studies an HFL architecture for energy-constrained UAVs in smart IoT systems, pioneering a solution to minimize global training cost increased caused by UAV disconnection. In light of this, we formulate a joint optimization problem involving learning configuration, bandwidth allocation, and device-to-UAV association, and perform global aggregation in time before UAV drops disconnect and redeployment of UAVs. The problem explicitly accounts for the dynamic nature of IoT devices and their interruptible communications and is unveiled to be NP-hard. To address this, we decompose it into three subproblems. First, we optimize the learning configuration and bandwidth allocation using an augmented Lagrangian function to reduce training costs. Second, we propose a device fitness score, integrating data heterogeneity (via Kullback-Leibler divergence), device-to-UAV distances, and IoT device resources, and develop a twin-delayed deep deterministic policy gradient (TD3)-based algorithm for dynamic device-to-UAV assignment. Third, We introduce a low-complexity two-stage greedy strategy for finding the location of UAVs redeployment and selecting the appropriate global aggregator UAV. Experiments on real-world datasets demonstrate significant cost reductions and robust performance under communication interruptions. © 2025, CC BY-NC-ND.

关键词： Federated learning

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Energy-Based Trajectory Tracking for Underactuated Mechanical systems: Velocity-Free and Disturbance Rejection Methods

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International Journal of Robust and Nonlinear Control 2025年

作者： Javanmardi, N. Borja, P. van der Schaft, A.J. Yazdanpanah, M.J. Scherpen, J.M.A. Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Jan C. Willems Center for Systems and Control Bernoulli Institute Faculty of Science and Engineering University of Groningen Groningen Netherlands

This work investigates three energy-shaping control approaches to address the trajectory-tracking problem for specific classes of underactuated mechanical systems. In particular, the notions of contractive systems and dynamic extensions are utilized to solve the trajectory-tracking problem while addressing implementation issues such as the lack of velocity sensors and the presence of constant disturbances. To this end, a first tracking controller is developed without the need for velocity measurements. A second controller is introduced, solving the trajectory-tracking problem while rejecting matched constant disturbances. Then, a third approach establishes conditions to combine both mentioned controllers. It is shown that the proposed design methods guarantee exponential convergence of closed-loop systems for specific classes of underactuated mechanical systems. The third method is illustrated with an example. © 2025 The Author(s). International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd.

关键词： Disturbance rejection

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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A semi-supervised multi-task assisted method for ultrasound medical image segmentation

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Neurocomputing 2025年 639卷

作者： Li, Honghe Yang, Jinzhu Qu, Mingjun Feng, Yong Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

The accurate segmentation of the left ventricle in echocardiography is critical for assessing cardiac function, but challenges such as blurred boundaries, high morphological variability, and limited annotated data hinder the performance of traditional methods. Recent advances in semi-supervised learning show promise in leveraging unlabeled data, yet existing techniques often rely on network or data-level perturbation, which may not fully exploit spatial and positional information essential for precise segmentation. To address these challenges, we propose a novel multi-task assisted semi-supervised segmentation framework. Our method combines segmentation, landmark detection, and image reconstruction into a unified model with a shared encoder and dual decoders. A feature cross-fusion module based on a cross-attention mechanism integrates features across tasks to enhance spatial and positional awareness. We have also introduced a contrastive learning mechanism to refine the segmentation boundaries, especially in areas where the edges are blurred. In addition, we utilize multi-scale supervision to better adapt the model to targets of different scales. The framework employs an exponential moving average student–teacher model to effectively utilize unlabeled data for training. Experiments on CAMUS and EchoNet-Dynamic datasets demonstrate that the proposed method achieves state-of-the-art performance, delivering near fully-supervised results with only 10%–20% labeled data. This highlights its potential for high-quality segmentation in low-annotation scenarios, outperforming existing semi-supervised learning methods. © 2025

关键词： Contrastive Learning

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing Data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. Data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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An interpretable convolutional neural network via generalized time–frequency scattering

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Signal Processing 2025年 237卷

作者： Liu, Xiaoping Chen, Gong Shi, Jun Tao, Ran School of Information and Electronics Beijing Institute of Technology and the Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Communication Research Center Harbin Institute of Technology Harbin150001 China

Convolutional neural networks (CNNs) have recently demonstrated impressive performance in complex machine learning tasks. However, the CNN requires a large quantity of annotated data to converge to a good solution, and the theoretical understanding of this network is still in its infancy. Towards this end, a variant of the CNN, dubbed the deep scattering network (DSN), has been proposed by employing the linear time–frequency transform. The DSN inherits the hierarchical structure of the CNN, but chooses predefined wavelet/Gabor filters as its convolutional kernels instead of data-driven linear filters. Unfortunately, the DSN suffers from a major drawback that it is suitable for stationary image textures but not for non-stationary image textures, since wavelet/Gabor filters are intrinsically linear translation-invariant filters. The aim of this paper is to overcome this deficiency based upon a generalized linear time–frequency transform–the short-time fractional Fourier transform (STFRFT) which can be interpreted as a bank of linear translation-variant filters and thus may be well suitable for non-stationary texture analysis. We first introduce a generalized time–frequency scattering transform using the STFRFT. By applying the derived result, we propose an interpretable CNN by cascading the STFRFTs and modulus operators. Moreover, several basic properties of the proposed interpretable CNN are derived, and an efficient implementation of this network is also presented. Finally, the applications of the derived results are discussed. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic Control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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LEARNING EFFICIENT POSITIONAL ENCODINGS WITH GRAPH NEURAL NETWORKS

arXiv

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arXiv 2025年

作者： Kanatsoulis, Charilaos I. Choi, Evelyn Jegelka, Stephanie Leskovec, Jure Ribeiro, Alejandro Dept. of Computer Science Stanford University United States School of Computation Information and Technology Technical University of Munich Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses significant challenges due to the absence of canonical node ordering and the scale of the graph. In this work, we identify four key properties that graph PEs should satisfy: stability, expressive power, scalability, and genericness. We find that existing eigenvector-based PE methods often fall short of jointly satisfying these criteria. To address this gap, we introduce PEARL, a novel framework of learnable PEs for graphs. Our primary insight is that message-passing GNNs function as nonlinear mappings of eigenvectors, enabling the design of GNN architectures for generating powerful and efficient PEs. A crucial challenge lies in initializing node attributes in a manner that is both expressive and permutation equivariant. We tackle this by initializing GNNs with random node inputs or standard basis vectors, thereby unlocking the expressive power of message-passing operations, while employing statistical pooling functions to maintain permutation equivariance. Our analysis demonstrates that PEARL approximates equivariant functions of eigenvectors with linear complexity, while rigorously establishing its stability and high expressive power. Experimental evaluations show that PEARL outperforms lightweight versions of eigenvector-based PEs and achieves comparable performance to full eigenvector-based PEs, but with one or two orders of magnitude lower complexity. Our code is available at https://***/ehejin/Pearl-PE. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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