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Cache Allocation in Multi-tenant Edge Computing: an Online Model-based Reinforcement Learning Approach

IEEE Transactions on Cloud Computing 2025年

作者： Ben-Ameur, Ayoub Araldo, Andrea Chahed, Tijani Dan, Gyorgy SAMOVAR Telecom SudParis Institut Polytechnique de Paris France KTH Royal Institute of Technology Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Sweden

We consider a Network Operator (NO) that owns Edge Computing (EC) resources, virtualizes them and lets third party Service Providers (SPs) run their services, using the allocated slice of resources. We focus on one specific resource, i.e., cache space, and on the problem of how to allocate it among several SPs in order to minimize the backhaul traffic. Due to confidentiality guarantees, the NO cannot observe the nature of the traffic of SPs, which is encrypted. Allocation decisions are thus challenging, since they must be taken solely based on observed monitoring information. Another challenge is that not all the traffic is cacheable. We propose a data-driven cache allocation strategy, based on Reinforcement Learning (RL). Unlike most RL applications, in which the decision policy is learned offline on a simulator, we assume no previous knowledge is available to build such a simulator. We thus apply RL in an online fashion, i.e., the model and the policy are learned by directly perturbing and monitoring the actual system. Since perturbations generate spurious traffic, we thus need to limit perturbations. This requires learning to be extremely efficient. To this aim, we devise a strategy that learns an approximation of the cost function, while interacting with the system. We then use such an approximation in a Model-Based RL (MB-RL) to speed up convergence. We prove analytically that our strategy brings cache allocation boundedly close to the optimum and stably remains in such an allocation. We show in simulations that such convergence is obtained within few minutes. We also study its fairness, its sensitivity to several scenario characteristics and compare it with a method from the state-of-the-art. © 2013 IEEE.

关键词： Contrastive Learning

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One-bit Compressed Sensing using Generative Models

arXiv

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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

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Orthogonal Time Frequency Space with Sub-Band Index Modulation

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IEEE Communications Letters 2025年第5期29卷 1047-1051页

作者： Zhang, Si-Yu Zhang, Yue-Xia Shahrrava, Behnam Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Key Laboratory of Information and Communication Systems Ministry of Information Industry School of Information and Communication Engineering Beijing100101 China University of Windsor Department of Electrical and Computer Engineerning Windsor N9B 3P4 Canada

This letter proposes an innovative orthogonal time-frequency space with sub-band index modulation (OTFS-SBIM) scheme. To overcome the complexity and spectral efficiency (SE) limitations inherent in the OTFS-IM, this study introduces a transmission mechanism where sub-carriers are equally divided into two sub-bands, and the index modulation (IM) operations are restricted to a single sub-band. To offset the potential SE loss, the proposed OTFS-SBIM employs a band-controlling bit β and performs IM independently in the in-phase and quadrature (I/Q) dimensions. These improvements ensure both the diversity gain and enhanced bit error rate (BER) performance. Further, a low-complex maximum likelihood (ML) detection is proposed for bit recovery. Finally, analytical expressions for average bit error probability (ABEP) and peak-to-average power ratio (PAPR) are derived and used to evaluate the OTFS-SBIM’s performance. Simulation results demonstrate that the proposed scheme outperforms the OTFS-IM scheme in terms of the BER and PAPR metrics while maintaining higher SE. © 1997-2012 IEEE.

关键词： Signal modulation

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High-Lift Prediction Workshop 5: High-Order Discretization Technology Focus Group Summary

High-Lift Prediction Workshop 5: High-Order Discretization T...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Galbraith, Marshall C. Ruiz-Gironés, Eloi Roca, Xevi Wang, Z.J. Holst, Kevin R. Skaperdas, Vangelis Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona Spain Department of Aerospace Engineering University of Kansas LawrenceKS66045 United States Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Customer Service BETA CAE Systems Thessaloniki Greece

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper summarizes the High-Order Technical Focus Group (HO-TFG) submissions forthe 5th AIAA High-Lift Prediction Workshop (HLPW-5). The goal of the workshop was to assess the state-of-the-art in mesh generation and computational fluid dynamics software for predicting peak lift of transport aircraft with flaps and slats deployed. Numerous geometric build ups of the Common Research Model High-Lift (CRM-HL) variant and Reynolds number considerations served as the primary focus of the workshop, and a simple wing-body variant of the CRM-HL was used as a verification test case. The HO-TFG was tasked with generating high-order curved meshes for these complex geometries, and computing high-order solutions using both Reynolds Averaged Navier-Stokes (RANS) and scale resolving Large Eddy Simulation(LES) formulations. While participation in the HO-TFG was limited, the effort produced curved meshes on highly-complex geometric configurations and computational results from two participants. The LES results demonstrate the potential for producing results comparable to 2nd-order accurate methods, but with fewer degrees of freedom. The RANS submission for this workshop does not demonstrate a benefit over 2nd-order schemes. However, the authors will argue a lack of understanding for optimal mesh distributions for high-order methods is the cause for the limited benefits observed for the RANS results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

引用

AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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Bayes and Biased Estimators Without Hyper-parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator

arXiv

引用

arXiv 2025年

作者： Ju, Yue Wahlberg, Bo Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimation. This paper focuses on ridge regression and the regularized estimator by employing the empirical Bayes hyperparameter estimator. We utilize the excess MSE to quantify the MSE difference between the empirical-Bayes-based regularized estimator and the maximum likelihood estimator for large sample sizes. We then exploit the excess MSE expressions to develop both a family of generalized Bayes estimators and a family of closed-form biased estimators. They have the same excess MSE as the empirical-Bayes-based regularized estimator but eliminate the need for hyper-parameter estimation. Moreover, we conduct numerical simulations to show that the performance of these new estimators is comparable to the empirical-Bayes-based regularized estimator, while computationally, they are more efficient. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Experimental assessment of a semi-submersible floating solar platform subjected to wave and wind loading 6th

Experimental assessment of a semi-submersible floating solar...

引用

6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Campbell, M. Baruah, G. Karimirad, M. Van, M. Pakrashi, V. School of Natural and Built Environment Queen’s University Belfast Belfast United Kingdom School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom UCD Centre for Mechanics Dynamical Systems and Risk Laboratory School of Mechanical and Materials Engineering University College Dublin Ireland

ISBN: (纸本)9781032905570

Floating solar platforms are subjected to coupled wave and wind induced responses, straddling the unpredictable wave-wind boundary layer. The simultaneous actions of these coupled responses in real sea conditions are not yet fully investigated for floating solar systems. The fluidstructure interactions are complex due to these coupled responses. The combined response to these wave and wind loads results in a coupled motion and tension in the mooring lines. This paper investigates the wave-only and wave-wind load case scenarios using scaled model wave tank testing. To study these scenarios, the Qualisys motion capture system is used to record the motions of the structure in the tank and load cells to record the mooring loads. This study investigates the correlation of wave, wind, motions, and mooring tension. The contribution of the wind loading component to the coupled wave-wind scenario is assessed and trends are determined for the wind loading contribution effects observed. © 2025 The Author(s).

关键词： Mooring

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ON INFINITE-HORIZON MINIMUM ENERGY CONTROL

arXiv

引用

arXiv 2025年

作者： Belabbas, Mohamed-Ali Chen, Xudong Electrical and Computer Engineering Department and Coordinated Science Laboratory University of Illinois Urbana-Champaign United States Electrical and Systems Engineering Washington University in St. Louis United States

We address the infinite-horizon minimum energy control problem for linear time-invariant finite-dimensional systems (A, B). We show that the problem admits a solution if and only if (A, B) is stabilizable and A does n... 详细信息

关键词： Linear time-invariant system

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Digital Logic, computer Architecture, and Dev Containers: Supporting Schools from Little to Large 2025

Digital Logic, Computer Architecture, and Dev Containers: Su...

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Proceedings of the 56th ACM Technical Symposium on computer science Education V. 2

作者： Bill Siever Michael Hall Jim Feher Roger Chamberlain Computer Science and Engineering Washington University in St. Louis St. Louis MO USA Computer Science and Engineering / Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA

ISBN: (纸本)9798400705328

Maintaining resources for courses in Digital Logic and computer Architecture can be challenging at any scale or institution, especially if such courses are intended to provide authentic experience with contemporary tools, like Hardware Description Languages (HDLs) and Field Programmable Gate Arrays (FPGAs). A recent redesign of an introductory Digital Logic and computer Architecture course at Washington University in St. Louis leveraged modern containerized environments, Codespaces and Development Containers, along with open-source tools to create a custom environment suited for hardware-focused courses in digital logic and computer architecture. This demo will introduce the system, which can easily be adopted by others. (Participants can create a full instance during the demonstration!) The environment is aligned with commonly used computer architecture texts and includes everything needed for a variety of assignments, including simulating designs (digital logic and assembly language) and deploying digital logic to low-cost FPGA platforms (

关键词： FPGAs

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

引用

Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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