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Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

IEEE Transactions on Services Computing 2025年

作者： Wu, Xiaohu Yu, Han Casale, Giuliano Gao, Guanyu Beijing University of Posts and Telecommunications National Engineering Research Center of Mobile Network Technologies Beijing China Nanyang Technological University WeBank-NTU Joint Research Centre on Fintech Singapore Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Imperial College London Department of Computing London United Kingdom Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

Premier cloud service providers (CSPs) offer on-demand and spot instances (i.e., virtual machines) with time-varying features in availability and price. While interacting with a CSP, what concerns users is the process of cost-effectively utilizing these instances, possibly in addition to self-owned instances. A job in data-intensive applications is represented by a directed acyclic graph (DAG) whose nodes represent tasks and the DAG can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we make some mild assumptions on the usage of various instances and give an analytical understanding of the expected behaviors while utilizing instances. Based on this, we propose informed heuristic policies to determine the allocation of deadlines and instances. The policies are parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, intuitive greedy policies are used as baselines to validate the effectiveness of the proposed analytical solutions. The cost improvement is up to 23.58% when spot and on-demand instances are considered and up to 50.08% when self-owned instances are also considered. © 2008-2012 IEEE.

关键词： Cloud platforms

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Multi-Agent Deep Reinforcement Learning-Based Cooperative Perception and Computation in VEC

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IEEE Internet of Things Journal 2025年

作者： Zhao, Liang Li, Longjia Tan, Zhiyuan Hawbani, Ammar He, Qiang Liu, Zhi Shenyang Aerospace University School of Computer Science Shenyang110136 China Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Northeastern University School of Medicine and Biological Information Engineering Shenyang110169 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

Connected and autonomous vehicles (CAVs) are an important paradigm of intelligent transportation systems. Cooperative perception (CP) and vehicular edge computing (VEC) enhance CAVs' perception capacity of the region of interest (RoI) while alleviating the pressure of intensive computation on onboard resources. However, existing CP and computation schemes are based on inefficient broadcast communications and still face challenges such as highly dynamic communication link channel conditions caused by vehicle mobility, and limited computing resources in VEC environments. Considering the delay sensitivity of CAVs' perception tasks and the need for enhanced perception, we propose a unicast-based cooperative perception and computation scheme to achieve more efficient resource utilization and perception task execution in VEC scenarios. Our goal is to maximize CP gain and minimize task execution delay by optimizing the decision of each ego CAVs. To solve the sequential decision-making problem of multi-objective optimization, we propose a solution based on improved multi-agent proximal policy optimization deep reinforcement learning, where CAVs agents make adaptive decisions distributed based on partial observations. Simulation results show that compared with the baseline algorithm, our proposed scheme effectively reduces the execution delay of ego CAVs perception tasks and ensures a high perception gain. © 2025 IEEE.

关键词： Deep reinforcement learning

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Complex Pythagorean Fuzzy Decision-Based Approach for Developing English Translation of the Scripture-Based Multiclustering Algorithms

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International Journal of Information Technology and Decision Making 2025年第2期24卷 473-533页

作者： Ahmed, Mohammed A. Zaidan, A.A. Qahtan, Sarah Alsattar, Hassan A. Zaidan, B.B. Mourad, Nahia Baharin, Hanif Kou, Gang Yatim, Khaironi Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi43600 Malaysia Network Engineering Department College of Engineering Al-Iraqia University Al Adhmia-Haiba Khaton Baghdad10053 Iraq SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia Information Technology Unit College of Health and Medical Technology-Baghdad Middle Technical University Baghdad Iraq Research Center Mazaya University College Nasiriyah Iraq MEU Research Unit Middle East University Amman Jordan Faculty of Engineering and IT British University in Dubai Dubai United Arab Emirates School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Computer and Information Sciences Faculty of Sciences and Information Technology Universiti Teknologi PETRONAS Seri Iskandar32610 Malaysia

The English Translation of the Quran Tafsir (ETQT) is essential to understanding and interpreting Allah's words. Clustering is a common text mining technique for eliciting meaningful knowledge from a text collection. It is commonly used when the selected datasets lack typical ground truths. To the best of our knowledge, no study has evaluated and benchmarked ETQTs to select the most comprehensive and appropriate one. The process of evaluating and benchmarking ETQTs falls under the multicriteria decision-making (MCDM) problem because of different issues, namely, multiple internal clustering validation criteria, data variation, and trade-offs between different criteria. The fuzzy decision by opinion score method (FDOSM) is one of the most recommended MCDM ranking methods in the literature to address the said issues. FDOSM has been extended under different fuzzy set (FS) environments to address issues of uncertainty and vagueness caused by expert feedback subjectivity. Although prior versions of FDOSM improved the uncertainty and vagueness issues, they remain open issues. Therefore, this paper extended FDOSM into the complex Pythagorean fuzzy decision by opinion score method (CPFDOSM) to evaluate and benchmark ETQTs. The proposed method consists of two main phases. The first phase formulates decision-matrix-based cluster algorithms and internal cluster validation criteria. The second phase (CPFDOSM development) prioritizes ETQTs and selects the optimum one. Data generation is performed on five different cluster algorithms and six internal cluster validation criteria using 16 ETQTs based on three decision-makers (DMs). Results show the following: (1) 6.25% of the individual decision-making results are identical among the three DMs, whereas 93.75% (n = 15/16) are different when I = 0.5 and I = 2. When I = 0.5. In addition, T7 has consistent ranks (Rank=16) across all DMs, whereas T14 has consistent ranks (Rank=1) across all DMs when I = 1. (2) The results of the group de

关键词： Clustering algorithms

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STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation

arXiv

引用

arXiv 2025年

作者： Morsali, Alireza Vaez, MohammadJavad Soltani, Hossein Kazerouni, Amirhossein Taati, Babak Mohammad-Noori, Morteza Department of Electrical Engineering McGill University Canada School of Mathematics Statistics and Computer Science University of Tehran Iran Department of Computer Science University of Toronto Canada Vector Institute Canada KITE Research Institute University Health Network Canada

Implicit Neural Representations (INRs) have emerged as a powerful framework for modeling continuous signals. The spectral bias of ReLU-based networks is a well-established limitation, restricting their ability to capture fine-grained details in target signals. While previous works have attempted to mitigate this issue through frequency-based encodings or architectural modifications, these approaches often introduce additional complexity and do not fully address the underlying challenge of learning high-frequency components efficiently. We introduce Sinusoidal Trainable Activation Functions (STAF), designed to directly tackle this limitation by enabling networks to adaptively learn and represent complex signals with higher precision and efficiency. STAF inherently modulates its frequency components, allowing for self-adaptive spectral learning. This capability significantly improves convergence speed and expressivity, making STAF highly effective for both signal representations and inverse problems. Through extensive evaluations, we demonstrate that STAF outperforms state-of-the-art (SOTA) methods in accuracy and reconstruction fidelity with superior Peak Signal-to-Noise Ratio (PSNR). These results establish STAF as a robust solution for overcoming spectral bias and the capacity-convergence gap, making it valuable for computer graphics and related fields. Our codebase is publicly accessible on the https://***/AlirezaMorsali/STAF. © 2025, CC BY.

关键词： Signal to noise ratio

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Trading Computing Power For Reducing Communication Loads: A Semantic Communication Perspective

引用

IEEE Transactions on Communications 2025年

作者： Zheng, Guangyuan Wen, Miaowen Xu, Lexi Ding, Zhiguo South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates China United Network Communications Corporation Research Institute Beijing100048 China Quzhou324003 China

As a new paradigm focusing on transmitting the meaning of information, semantic communications (SCs) have been revealed significant potential in alleviating network congestion and improving energy efficiency. By extracting a small-size semantic feature from the large-size raw-data, the communication loads can be reduced at a price of more computing power, i.e., the computational resource used in semantic extraction. In this paper, we explore the computation dimension to improve the communication performance in an uplink SC system. The SC-oriented user first compresses its original data via local computing during other users’ transmission time and then transmits it to the base station within the assigned time. To achieve a balanced tradeoff between communications and computing, we formulate an optimization problem to minimize the energy consumption of all users by jointly considering the compression ratio and time allocation. We first propose an efficient general algorithm that can be applied to different SC models. To gain more insights, we then derive the closed-form solutions for two special cases: equal-time allocation and two-user transmission. The obtained analytical results reveal a pronounced energy saving of adopting SC, especially for large transmitted data size, high transmission energy coefficient, scarce time resources, and poor channel conditions. Simulation results reveal that our proposed SC-based scheme achieves excellent performance in terms of saving energy consumption compared to the conventional communication. © 2025 IEEE. All rights reserved.

关键词： Energy utilization

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An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study

arXiv

引用

arXiv 2025年

作者： Falahati, Sonya Alizadeh, Morteza Safahi, Zhino Khaledian, Navid Meybodi, Mohsen Alambardar Salmanpour, Mohammad R. VancouverBC Canada Electrical and Computer Engineering Department Nooshirvani University of Technology Babol Iran Department of Mathematics University of Isfahan Isfahan Iran Social & Biological Network Laboratory Department of Computer Engineering University of Kurdistan Sanandaj Iran University of Luxembourg Esch-sur-Alzette Luxembourg Department of Applied Mathematics and Computer Science University of Isfahan Isfahan Iran Department of Integrative Oncology BC Cancer Research Institute Vancouver Canada

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture of HERMES, with a focus on deployment in high-traffic public areas. Methods: HERMES analyzes self-reported symptoms and anonymized medical histories using AI algorithms to generate context-aware OTC medication recommendations. The system was initially trained using Electronic Health Records (EHR) from the MIMIC-III dataset (6,350 patients) and Drug-Drug Interaction (DDI) data from the TWOSIDES database, incorporating the top 90 severity DDI types. Real-time DDI checks and ATC-mapped drug codes further improve safety. The kiosk is designed for accessibility, offering multilingual support, large fonts, voice commands, and Braille compatibility. A built-in health education library promotes preventive care and health literacy. A survey was conducted among 10 medical professionals to evaluate its potential applications in medicine. Results: Preliminary results show that the enhanced GAMENet model achieved a Precision-Recall AUC (PRAUC) of 0.74, outperforming the original model. These findings suggest a strong potential for delivering accurate and secure healthcare recommendations in public settings. Conclusion: HERMES demonstrates how AI-driven, privacy-preserving kiosks can enhance public health access, empower users, and alleviate burdens on healthcare systems. Future work will focus on real-world deployment, usability testing, and scalability for broader adoption. © 2025, CC BY-NC-ND.

关键词： Personalized medicine

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Cost-efficient FEC Scheme for Time-sensitive Multi-hop Transmissions in Overlay networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Chao Wang, Jessie Hui Li, Rui Wu, Hao Wang, Jilong Zhang, Jun Zheng, Kai Tsinghua University Institute for Network Sciences and Cyberspace BNRist Beijing China Huawei Technologies Co. Ltd. China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

In pursuit of low latency, real-time communication (RTC) service providers usually use multi-hop overlay links worldwide to bypass congested links, especially for medium- and long-distance transmissions. In such multi-hop long-distance transmission scenarios, utilizing retransmission to recover lost packets can result in increased end-to-end latency. Therefore, Forward Error Correction (FEC) is viewed as a promising way to solve the loss problem. However, for multi-hop overlay transmission, existing FEC schemes either introduce a non-negligible processing delay at each hop or reduce the processing delay at the cost of a high coefficient overhead. In this work, we propose a multi-hop FEC scheme, i.e., FEC-OEM, which considers both processing delay and coefficient overhead. FEC-OEM is designed based on two observations we obtained from measurements. First, coefficient overhead can only be reduced through an implicit transmission way. Therefore, we design a modulation-based recoding module that enables implicit coefficient transmission and hop-by-hop recoding at the same time. Second, using on-the-fly computation is a promising way to reduce processing delay. Accordingly, we design an elimination method to make the modulation-based recoding can be carried out on-the-fly. Real-world experiments demonstrate that FEC-OEM can reduce the processing delay by up to 88% without increasing the coefficient overhead compared to state-of-the-art schemes. We also use FEC-OEM to transmit packets for applications with different loss tolerances, and the results show that FEC-OEM can improve the QoE more effectively than state-of-the-art coding schemes. © 2002-2012 IEEE.

关键词： Forward error correction

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

引用

2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Lalitha, T. Szymański, Jerzy Ryszard Zurek-Mortka, Marta Sathiyanarayanan, Mithileysh Srilekha, K.R. Presidency University School of Information Science Karnataka Bengaluru India Casimir Pulaski University of Radom Faculty of Transport Electrical Engineering and Computer Sciences Department of Electrical Drives and Industrial Electronics Poland Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pulaskiego Radom Poland MIT Square London United Kingdom Vit University Tamilnadu Vellore India

ISBN: (纸本)9798331513894

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal's death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis. © 2025 IEEE.

关键词： Low emission

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Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Nhan Vo, Van Quoc Long, Nguyen Dang, Viet-Hung Dac Ho, Tu Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Duy Tan University Faculty of Information Technology Da Nang550000 Viet Nam Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Information Security and Communication Technology Trondheim7034 Norway National Economics University DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology Hanoi12116 Viet Nam University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Esch-sur-Alzette4365 Luxembourg Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok10900 Thailand Khon Kaen University Applied Network Technology College of Computing Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

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