Tootfinder

Waterfilling at the Edge: Optimal Percentile Resource Allocation via Risk-Averse Reduction
Gokberk Yaylali, Ahmad Ali Khan, Dionysios S. Kalogerias
https://arxiv.org/abs/2507.10838

Waterfilling at the Edge: Optimal Percentile Resource Allocation via Risk-Averse Reduction
We address deterministic resource allocation in point-to-point multi-terminal AWGN channels without inter-terminal interference, with particular focus on optimizing quantile transmission rates for cell-edge terminal service. Classical utility-based approaches -- such as minimum rate, sumrate, and proportional fairness -- are either overconservative, or inappropriate, or do not provide a rigorous and/or interpretable foundation for fair rate optimization at the edge. To overcome these challenges…

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN
Adhwaa Alchaab, Ayman Younis, Dario Pompili
https://arxiv.org/abs/2507.11499

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN
Next-Generation Radio Access Networks (NGRAN) aim to support diverse vertical applications with strict security, latency, and Service-Level Agreement (SLA) requirements. These demands introduce challenges in securing the infrastructure, allocating resources dynamically, and enabling real-time reconfiguration. This demo presents SnSRIC, a secure and intelligent network slicing framework that mitigates a range of Distributed Denial-of-Service (DDoS) attacks in Open RAN environments. SnSRIC incorp…

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
Heyang Huang, Cunchen Hu, Jiaqi Zhu, Ziyuan Gao, Liangliang Xu, Yizhou Shan, Yungang Bao, Sun Ninghui, Tianwei Zhang, Sa Wang
https://arxiv.org/abs/2506.13497

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
The Text-to-Video (T2V) model aims to generate dynamic and expressive videos from textual prompts. The generation pipeline typically involves multiple modules, such as language encoder, Diffusion Transformer (DiT), and Variational Autoencoders (VAE). Existing serving systems often rely on monolithic model deployment, while overlooking the distinct characteristics of each module, leading to inefficient GPU utilization. In addition, DiT exhibits varying performance gains across different resoluti…

Byzantine-Resilient Decentralized Online Resource Allocation
Runhua Wang, Qing Ling, Hoi-To Wai, Zhi Tian
https://arxiv.org/abs/2508.08658 https://arxiv.or…

Byzantine-Resilient Decentralized Online Resource Allocation
In this paper, we investigate the problem of decentralized online resource allocation in the presence of Byzantine attacks. In this problem setting, some agents may be compromised due to external manipulations or internal failures, causing them to behave maliciously and disrupt the resource allocation process by sending incorrect messages to their neighbors. Given the non-consensual nature of the resource allocation problem, we formulate it under a primal-dual optimization framework, where the …

Reasoning about Medical Triage Optimization with Logic Programming
Jaikrishna Manojkumar Patil, Adam Chapman, Richard Knuszka, John Chapman, Paulo Shakarian
https://arxiv.org/abs/2507.10781

Reasoning about Medical Triage Optimization with Logic Programming
We present a logic programming framework that orchestrates multiple variants of an optimization problem and reasons about their results to support high-stakes medical decision-making. The logic programming layer coordinates the construction and evaluation of multiple optimization formulations, translating solutions into logical facts that support further symbolic reasoning and ensure efficient resource allocation-specifically targeting the "right patient, right platform, right escort, right tim…

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
Minwoo Kim, Youngchol Choi, Yeongjun Kim, Eojin Seo, Hyun Jong Yang
https://arxiv.org/abs/2506.13008

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, {the} low propagation speed of UWA signals, combined w…

LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences
Sankarshan Damle, Boi Faltings
https://arxiv.org/abs/2508.06060 https://

LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences
Large Language Models (LLMs) are increasingly expected to handle complex decision-making tasks, yet their ability to perform structured resource allocation remains underexplored. Evaluating their reasoning is also difficult due to data contamination and the static nature of existing benchmarks. We present a dual-purpose framework leveraging Participatory Budgeting (PB) both as (i) a practical setting for LLM-based resource allocation and (ii) an adaptive benchmark for evaluating their reasoning…

MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
Soheil Mahdizadeh, Elyas Oustad, Mohsen Ansari
https://arxiv.org/abs/2507.12028

MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and…

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci
https://arxiv.org/abs/2506.12074

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Mobile traffic prediction is an important enabler for optimizing resource allocation and improving energy efficiency in mobile wireless networks. Building on the advanced contextual understanding and generative capabilities of large language models (LLMs), this work introduces a context-aware wireless traffic prediction framework powered by LLMs. To further enhance prediction accuracy, we leverage in-context learning (ICL) and develop a novel two-step demonstration selection strategy, optimizin…

Toward Automated Hypervisor Scenario Generation Based on VM Workload Profiling for Resource-Constrained Environments
Hyunwoo Kim, Jaeseong Lee, Sunpyo Hong, Changmin Han
https://arxiv.org/abs/2508.08952

Toward Automated Hypervisor Scenario Generation Based on VM Workload Profiling for Resource-Constrained Environments
In the automotive industry, the rise of software-defined vehicles (SDVs) has driven a shift toward virtualization-based architectures that consolidate diverse automotive workloads on a shared hardware platform. To support this evolution, chipset vendors provide board support packages (BSPs), hypervisor setups, and resource allocation guidelines. However, adapting these static configurations to varying system requirements and workloads remain a significant challenge for Tier 1 integr…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
Tasfia Mashiat, Patrick J. Fowler, Sanmay Das
https://arxiv.org/abs/2508.08573 htt…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial d…

Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation
Roberto Rocco, Simone Rizzo, Matteo Barbieri, Gabriella Bettonte, Elisabetta Boella, Fulvio Ganz, Sergio Iserte, Antonio J. Pe\~na, Petter Sand\r{a}s, Alberto Scionti, Olivier Terzo, Chiara Vercellino, Giacomo Vitali, Paolo Viviani, Jonathan Frassineti, Sara Marzella, Daniele Ottaviani, Iacopo Colonnelli, Daniele Gregori

Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation
The integration of quantum computers within classical High-Performance Computing (HPC) infrastructures is receiving increasing attention, with the former expected to serve as accelerators for specific computational tasks. However, combining HPC and quantum computers presents significant technical challenges, including resource allocation. This paper presents a novel malleability-based approach, alongside a workflow-based strategy, to optimize resource utilization in hybrid HPC-quantum workloads…

Resource Allocation for Multi-waveguide Pinching Antenna-assisted Broadcast Networks
Ruotong Zhao, Shaokang Hu, Deepak Mishra, Derrick Wing Kwan Ng
https://arxiv.org/abs/2507.03915

Resource Allocation for Multi-waveguide Pinching Antenna-assisted Broadcast Networks
In this paper, we investigate the resource allocation for multi-dielectric waveguide-assisted broadcast systems, where each waveguide employs multiple pinching antennas (PAs), aiming to maximize the minimum achievable rate among multiple users. To capture realistic propagation effects, we propose a novel generalized frequency-dependent power attenuation model for dielectric waveguides PA system. We jointly optimize waveguide beamforming, PA power allocation, and antenna positions via a block co…

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Dejun Xu, Jijia Chen, Gary G. Yen, Min Jiang
https://arxiv.org/abs/2506.06362

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Bilevel optimization poses a significant computational challenge due to its nested structure, where each upper-level candidate solution requires solving a corresponding lower-level problem. While evolutionary algorithms (EAs) are effective at navigating such complex landscapes, their high resource demands remain a key bottleneck -- particularly the redundant evaluation of numerous unpromising lower-level tasks. Despite recent advances in multitasking and transfer learning, resource waste persis…

The Price of EF1 for Few Agents with Additive Ternary Valuations
Maria Kyropoulou, Alexandros A. Voudouris
https://arxiv.org/abs/2508.09869 https://arxiv.o…

The Price of EF1 for Few Agents with Additive Ternary Valuations
We consider a resource allocation problem with agents that have additive ternary valuations for a set of indivisible items, and bound the price of envy-free up to one item (EF1) allocations. For a large number $n$ of agents, we show a lower bound of $Ω(\sqrt{n})$, implying that the price of EF1 is no better than when the agents have general subadditive valuations. We then focus on instances with few agents and show that the price of EF1 is $12/11$ for $n=2$, and between $1.2$ and $1.256$ for $…

On the Convergence of a Noisy Gradient Method for Non-convex Distributed Resource Allocation: Saddle Point Escape
Lei Qin, Ye Pu
https://arxiv.org/abs/2508.06922 https://…

On the Convergence of a Noisy Gradient Method for Non-convex Distributed Resource Allocation: Saddle Point Escape
This paper considers a class of distributed resource allocation problems where each agent privately holds a smooth, potentially non-convex local objective, subject to a globally coupled equality constraint. Built upon the existing method, Laplacian-weighted Gradient Descent, we propose to add random perturbations to the gradient iteration to enable efficient escape from saddle points and achieve second-order convergence guarantees. We show that, with a sufficiently small fixed step size, the it…

Movable-Antenna Array Enhanced Downlink NOMA
Nianzu Li, Peiran Wu, Lipeng Zhu, Derrick Wing Kwan Ng
https://arxiv.org/abs/2506.11438 https://

Movable-Antenna Array Enhanced Downlink NOMA
Movable antenna (MA) has gained increasing attention in the field of wireless communications due to its exceptional capability to proactively reconfigure wireless channels via localized antenna movements. In this paper, we investigate the resource allocation design for an MA array-enabled base station serving multiple single-antenna users in a downlink non-orthogonal multiple access (NOMA) system. We aim to maximize the sum rate of all users by jointly optimizing the transmit beamforming and th…

Diffusion-RL for Scalable Resource Allocation for 6G Networks
Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri
https://arxiv.org/abs/2506.07880 …

Diffusion-RL for Scalable Resource Allocation for 6G Networks
This paper presents a novel approach to resource allocation in Open Radio Access Networks (O-RAN), leveraging a Generative AI technique with network slicing to address the diverse demands of 5G and 6G service types such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and Massive Machine-Type Communications (mMTC). Additionally, we provide a comprehensive analysis and comparison of machine learning (ML) techniques for resource allocation within O-RAN, eval…

Semantic Communications in 6G: Coexistence, Multiple Access, and Satellite Networks
Ishtiaque Ahmed, Yingzhuo Sun, Jingwen Fu, Alper Kose, Leila Musavian, Ming Xiao, Berna Ozbek
https://arxiv.org/abs/2506.11779

Semantic Communications in 6G: Coexistence, Multiple Access, and Satellite Networks
The exponential growth of wireless users and bandwidth constraints necessitates innovative communication paradigms for next-generation networks. Semantic Communication (SemCom) emerges as a promising solution by transmitting extracted meaning rather than raw bits, enhancing spectral efficiency and enabling intelligent resource allocation. This paper explores the integration of SemCom with conventional Bit-based Communication (BitCom) in heterogeneous networks, highlighting key challenges and op…

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Rajpreet Singh, Novak Bo\v{s}kov, Aditya Gudal, Manzoor A. Khan
https://arxiv.org/abs/2506.01571

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extendin…

PAPR of DFT-s-OTFS with Pulse Shaping
Jialiang Zhu, Sanoopkumar P. S., Arman Farhang
https://arxiv.org/abs/2507.12210 https://arxiv.o…

PAPR of DFT-s-OTFS with Pulse Shaping
Orthogonal Time Frequency Space (OTFS) suffers from high peak-to-average power ratio (PAPR) when the number of Doppler bins is large. To address this issue, a discrete Fourier transform spread OTFS (DFT-s-OTFS) scheme is employed by applying DFT spreading across the Doppler dimension. This paper presents a thorough PAPR analysis of DFT-s-OTFS in the uplink scenario using different pulse shaping filters and resource allocation strategies. Specifically, we derive a PAPR upper bound of DFT-s-OTFS …

This https://arxiv.org/abs/2310.03475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Fair Division with Allocator's Preference
We study the fair allocation of indivisible resources among agents. Most prior work focuses on fairness and/or efficiency among agents. However, the allocator, as the resource owner, may also be involved in many scenarios (e.g., government resource allocation, heritage division, company personnel assignment, etc). The allocator inclines to obtain a fair or efficient allocation based on her preference over the items and to whom each item is allocated. We propose a model and study two problems: 1…

Joint Scheduling and Resource Allocation in mmWave IAB Networks Using Deep RL
Maryam Abbasalizadeh, Sashank Narain
https://arxiv.org/abs/2508.07604 https://

Joint Scheduling and Resource Allocation in mmWave IAB Networks Using Deep RL
Integrated Access and Backhaul (IAB) is critical for dense 5G and beyond deployments, especially in mmWave bands where fiber backhaul is infeasible. We propose a novel Deep Reinforcement Learning (DRL) framework for joint link scheduling and resource slicing in dynamic, interference-prone IAB networks. Our method integrates a greedy Double Deep Q-Network (DDQN) scheduler to activate access and backhaul links based on traffic and topology, with a multi-agent DDQN allocator for bandwidth and ante…

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
Rashika Raina, Nidhi Simmons, David E. Simmons, Michel Daoud Yacoub, Trung Q. Duong
https://arxiv.org/abs/2507.17494

To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networks
In next-generation communications and networks, machine learning (ML) models are expected to deliver not only accurate predictions but also well-calibrated confidence scores that reflect the true likelihood of correct decisions. This paper studies the calibration performance of an ML-based outage predictor within a single-user, multi-resource allocation framework. We first establish key theoretical properties of this system's outage probability (OP) under perfect calibration. Importantly, we sh…

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04947

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents whose preferences deviate from traditional expected utility theory (EUT), specifically incorporating cumulative prospect theory (CPT) preferences. We develop a comprehensive analytical framework that captures human-centric aspects of decision-making and risky…

Reducing Cartel Violence: The Mexican Dilemma Between Social and Security Spending
Rafael Prieto-Curiel, Dieter Grass, Stefan Wrzaczek, Gian Maria Campedelli, Gernot Tragler, Gustav Feichtinger
https://arxiv.org/abs/2508.06509

Reducing Cartel Violence: The Mexican Dilemma Between Social and Security Spending
Organised crime in Mexico threatens societal stability and public safety, driving pervasive violence and economic disruption. Despite security investments and social programs designed in part to reduce involvement in crime, cartel power and violence continue to persist. This study evaluates existing policies and introduces a novel framework using optimal control theory to analyse cartel dynamics. Specifically, by modelling resource allocation between security measures and social programs, we id…

Radio Map Assisted Routing and Predictive Resource Allocation over Dynamic Low Altitude Networks
Bowen Li, Junting Chen
https://arxiv.org/abs/2507.19111 https://

Radio Map Assisted Routing and Predictive Resource Allocation over Dynamic Low Altitude Networks
Dynamic low altitude networks offer significant potential for efficient and reliable data transport via unmanned aerial vehicles (UAVs) relays which usually operate with predetermined trajectories. However, it is challenging to optimize the data routing and resource allocation due to the time-varying topology and the need to control interference with terrestrial systems. Traditional schemes rely on time-expanded graphs with uniform and fine time subdivisions, making them impractical for interfe…

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo
https://arxiv.org/abs/2506.05693

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
Horizontal Pod Auto-scalers (HPAs) are crucial for managing resource allocation in microservice architectures to handle fluctuating workloads. However, traditional HPAs fail to address resource disruptions caused by faults, cyberattacks, maintenance, and other operational challenges. These disruptions result in resource wastage, service unavailability, and HPA performance degradation. To address these challenges, we extend our prior work on Smart HPA and propose SecureSmart HPA, which offers re…

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
Wenwen Wu, Shanying Zhu, Cailian Chen, Xinping Guan
https://arxiv.org/abs/2508.02164

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
This paper considers distributed resource allocation problems (DRAPs) with a coupled constraint for real-time systems. Based on primal-dual methods, we adopt a control perspective for optimization algorithm design by synthesizing a safe feedback controller using control barrier functions to enforce constraint satisfaction. On this basis, a distributed anytime-feasible resource allocation (DanyRA) algorithm is proposed. It is shown that DanyRA algorithm converges to the exact optimal solution of…

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
Berire Gunes Reyhan, Sinem Coleri
https://arxiv.org/abs/2507.08653

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, tra…

The Secure Overview and Analysis OF 3GPP MAC CE
Jin Cao, Yuanyuan Yang, Ruhui Ma, Sheng Li, Hui Li
https://arxiv.org/abs/2506.09502 https://

The Secure Overview and Analysis OF 3GPP MAC CE
To more effectively control and allocate network resources, MAC CE has been introduced into the network protocol, which is a type of control signaling located in the MAC layer. Since MAC CE lacks encryption and integrity protection mechanisms provided by PDCP, the control signaling carried by MAC CE is vulnerable to interception or tampering by attackers during resource scheduling and allocation. Currently, the 3GPP has analyzed the security risks of Layer 1/Layer 2 Triggered Mobility (LTM), wh…

Strategies for Resource Allocation of Two Competing Companies using Genetic Algorithm
Wing Keung Cheung, Kwok Yip Szeto
https://arxiv.org/abs/2507.02952 ht…

Strategies for Resource Allocation of Two Competing Companies using Genetic Algorithm
We investigate various strategic locations of shops in shopping malls in a metropolis with the aim of finding the best strategy for final dominance of market share by a company in a competing environment. The problem is posed in the context of two competing supermarket chains in a metropolis, described in the framework of the two-dimensional Ising model. Evolutionary Algorithm is used to encode the ensemble of initial configurations and Monte Carlo method is used to evolve the pattern. Numerica…

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04952

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
The problem of resource allocation in goal-oriented semantic communication with semantic-aware utilities and subjective risk perception is studied here. By linking information importance to risk aversion, we model agent behavior using Cumulative Prospect Theory (CPT), which incorporates risk-sensitive utility functions and nonlinear transformations of distributions, reflecting subjective perceptions of gains and losses. The objective is to maximize the aggregate utility across multiple CPT-mode…

Direction Estimation of Sound Sources Using Microphone Arrays and Signal Strength
Mahdi Ali Pour, Utku Gunay Acer
https://arxiv.org/abs/2507.03466 https://…

Direction Estimation of Sound Sources Using Microphone Arrays and Signal Strength
Sound-tracking refers to the process of determining the direction from which a sound originates, making it a fundamental component of sound source localization. This capability is essential in a variety of applications, including security systems, acoustic monitoring, and speaker tracking, where accurately identifying the direction of a sound source enables real-time responses, efficient resource allocation, and improved situational awareness. While sound-tracking is closely related to localiza…

Toward Energy and Location-Aware Resource Allocation in Next Generation Networks
Mandar Datar (CEA-LETI), Mattia Merluzzi (CEA-LETI)
https://arxiv.org/abs/2508.05109 https://

Toward Energy and Location-Aware Resource Allocation in Next Generation Networks
Wireless networks are evolving from radio resource providers to complex systems that also involve computing, with the latter being distributed across edge and cloud facilities. Also, their optimization is shifting more and more from a performance to a value-oriented paradigm. The two aspects shall be balanced continuously, to maximize the utilities of Services Providers (SPs), users quality of experience and fairness, while meeting global constraints in terms of energy consumption and carbon fo…

Efficient Resource Allocation under Adversary Attacks: A Decomposition-Based Approach
Mansoor Davoodi, Setareh Maghsudi
https://arxiv.org/abs/2506.23442 ht…

Efficient Resource Allocation under Adversary Attacks: A Decomposition-Based Approach
We address the problem of allocating limited resources in a network under persistent yet statistically unknown adversarial attacks. Each node in the network may be degraded, but not fully disabled, depending on its available defensive resources. The objective is twofold: to minimize total system damage and to reduce cumulative resource allocation and transfer costs over time. We model this challenge as a bi-objective optimization problem and propose a decomposition-based solution that integrate…

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems
Mingjie Bi, Ilya Kovalenko, Dawn M. Tilbury, Kira Barton
https://arxiv.org/abs/2507.19043

Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems
The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g., machine breakdown) occurs. Though multi-agent methods have been proposed …

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
Jing Wang, Amar Sra, Jeremy C. Weiss
https://arxiv.org/abs/2506.22444

Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events, such as hospitalization and reinfection, is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text t…

Graph Diffusion-Based AeBS Deployment and Resource Allocation for RSMA-Enabled URLLC Low-Altitude Economy Networks
Xudong Wang, Lei Feng, Jiacheng Wang, Hongyang Du, Changyuan Zhao, Wenjing Li, Zehui Xiong, Dusit Niyato, Ping Zhang
https://arxiv.org/abs/2507.04081

Graph Diffusion-Based AeBS Deployment and Resource Allocation for RSMA-Enabled URLLC Low-Altitude Economy Networks
As a key component of low-altitude economic networks, aerial base stations (AeBSs) provide flexible and reliable wireless coverage to support 6G ultra-reliable and low-latency communication (URLLC) services. However, limited spectrum resources and severe co-channel interference pose significant challenges to the deployment and resource allocation of AeBSs. To address these limitations, this paper proposes a novel rate-splitting multiple access (RSMA)-enabled transmission design to flexibly mana…

Resource Allocation for Pinching-Antenna Systems: State-of-the-Art, Key Techniques and Open Issues
Ming Zeng, Ji Wang, Octavia A. Dobre, Zhiguo Ding, George K. Karagiannidis, Robert Schober, H. Vincent Poor
https://arxiv.org/abs/2506.06156

Resource Allocation for Pinching-Antenna Systems: State-of-the-Art, Key Techniques and Open Issues
Pinching antennas have emerged as a promising technology for reconfiguring wireless propagation environments, particularly in high-frequency communication systems operating in the millimeter-wave and terahertz bands. By enabling dynamic activation at arbitrary positions along a dielectric waveguide, pinching antennas offer unprecedented channel reconfigurability and the ability to provide line-of-sight (LoS) links in scenarios with severe LoS blockages. The performance of pinching-antenna syste…

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests
Daniel Str\"ombom, Julianna Hoitt, Jinrong Hu, Swati Pandey, Elizabeth Batchelar
https://arxiv.org/abs/2508.01117

Effective resource allocation to combat invasions of the spotted lanternfly (Lycorma delicatula) and similar pests
The spotted lanternfly is rapidly establishing itself as a major insect pest with global implications. Despite significant management efforts, its spread continues in invaded regions, and refined management strategies are required. A recent study introduced a model that generalized the results of empirical control efficacy studies by incorporating population dynamics and incomplete delivery. In particular, a generalized population growth formula was derived, providing the minimum proportion of …

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu
https://arxiv.org/abs/2506.14125

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome li…

HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler
https://arxiv.org/abs/2508.09184 http…

HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Cellular traffic forecasting is essential for network planning, resource allocation, or load-balancing traffic across cells. However, accurate forecasting is difficult due to intricate spatial and temporal patterns that exist due to the mobility of users. Existing AI-based traffic forecasting models often trade-off accuracy and computational efficiency. We present Hierarchical SpatioTemporal Mamba (HiSTM), which combines a dual spatial encoder with a Mamba-based temporal module and attention me…

Evolutionary learning of microbial populations in partially predictable environments
Roaa Mohmmed Yagb Omer, Onofrio Mazzarisi, Martina Dal Bello, Jacopo Grilli
https://arxiv.org/abs/2508.00527

Evolutionary learning of microbial populations in partially predictable environments
Populations evolving in fluctuating environments face the fundamental challenge of balancing adaptation to current conditions against preparation for uncertain futures. Here, we study microbial evolution in partially predictable environments using proteome allocation models that capture the trade-off between growth rate and lag time during environmental transitions. We demonstrate that evolution drives populations toward an evolutionary stable allocation strategy that minimizes resource depleti…

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation
Suho Kang, Ziyang Liu, Rajan Udwani
https://arxiv.org/abs/2507.14812 https:/…

A Black-Box Approach for Exogenous Replenishment in Online Resource Allocation
In a typical online resource allocation problem, we start with a fixed inventory of resources and make online allocation decisions in response to resource requests that arrive sequentially over a finite horizon. We consider settings where the inventory is replenished over time according to an unknown exogenous process. We introduce black-box methods that extend any existing algorithm, originally designed without considering replenishment, into one that works with an arbitrary (adversarial or st…

Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation
Zhiwen Zhou
https://arxiv.org/abs/2508.06428 https://

Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation
Low-altitude unmanned aerial vehicles (UAVs) are expected to play an important role for low-altitude economy with a wide range of applications like precise agriculture, aerial delivery and surveillance. Integrated sensing and communication (ISAC) is a key technology to enable the large-scale deployment and routine usage of UAVs by providing both communication and sensing services efficiently. For UAV ISAC systems, as UAV often acts as both a communication user equipment (UE) and a sensing targe…

Digital-Twin Empowered Site-Specific Radio Resource Management in 5G Aerial Corridor
Pulok Tarafder, Imtiaz Ahmed, Danda B. Rawat, Md. Zoheb Hassan, Kamrul Hasan
https://arxiv.org/abs/2507.04566

Digital-Twin Empowered Site-Specific Radio Resource Management in 5G Aerial Corridor
Base station (BS) association and beam selection in multi-cell drone corridor networks present unique challenges due to the high altitude, mobility and three-dimensional movement of drones. These factors lead to frequent handovers and complex beam alignment issues, especially in environments with dense BS deployments and varying signal conditions. To address these challenges, this paper proposes a channel-twin (CT) enabled resource-allocation framework for drone-corridor communications, where t…

Leveraging Relational Evidence: Population Size Estimation on Tree-Structured Data with the Weighted Multiplier Method
Mallory J Flynn, Paul Gustafson
https://arxiv.org/abs/2506.21020

Leveraging Relational Evidence: Population Size Estimation on Tree-Structured Data with the Weighted Multiplier Method
Populations of interest are often hidden from data for a variety of reasons, though their magnitude remains important in determining resource allocation and appropriate policy. One popular approach to population size estimation, the multiplier method, is a back-calculation tool requiring only a marginal subpopulation size and an estimate of the proportion belonging to this subgroup. Another approach is to use Bayesian methods, which are inherently well-suited to incorporating multiple data sour…

This https://arxiv.org/abs/2204.07330 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems
This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against a potential attacker with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve c…

RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes
Jiaxing Wang, Yifeng Yu, Jiahan Song, Bin Cao, Jing Fan, Ji Zhang
https://arxiv.org/abs/2507.02690

RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes
Next activity prediction represents a fundamental challenge for optimizing business processes in service-oriented architectures such as microservices environments, distributed enterprise systems, and cloud-native platforms, which enables proactive resource allocation and dynamic service composition. Despite the prevalence of sequence-based methods, these approaches fail to capture non-sequential relationships that arise from parallel executions and conditional dependencies. Even though graph-ba…

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
Oleksiy Ostapenko, Charles Guille-Escuret, Luke Kumar, Max Tian, Denis Kocetkov, Gopeshh Subbaraj, Raymond Li, Joel Lamy-Poirier, Sebastien Paquet, Torsten Scholak
https://arxiv.org/abs/2507.22250

Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
We introduce a framework for optimizing domain-specific dataset construction in foundation model training. Specifically, we seek a cost-efficient way to estimate the quality of data sources (e.g. synthetically generated or filtered web data, etc.) in order to make optimal decisions about resource allocation for data sourcing from these sources for the stage two pre-training phase, aka annealing, with the goal of specializing a generalist pre-trained model to specific domains. Our approach exten…

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
Tao Yu, Simin Wang, Shunqing Zhang, Xiaojing Chen, Zi Xu, Xin Wang, Jiandong Li, Junyu Liu, Sihai Zhang
https://arxiv.org/abs/2506.03508

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or dat…

This https://arxiv.org/abs/2501.14417 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DeepServe: Serverless Large Language Model Serving at Scale
In this paper, we propose DEEPSERVE, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DEEPSERVE addresses key challenges such as resource allocation, serving efficiency, and cold start latencies through four main design components. First, DEEPSERVE uses a simple serverless abstraction called the request-job-task model, which helps manage diverse AI workloads across posttraining and model-serving tasks. Second, DEEPS…

A Heuristic Method for Simplified Resource Allocation based on Comparative Advantage in Wireless Access Systems
Lin Cheng, Bernardo A. Huberman
https://arxiv.org/abs/2508.01824 …

A Heuristic Method for Simplified Resource Allocation based on Comparative Advantage in Wireless Access Systems
This paper presents a heuristic method for simplifying resource allocation in access systems, leveraging the concept of comparative advantage to reduce computational complexity while maintaining near-optimal performance. Using power-division non-orthogonal multiple access (PD-NOMA) as an example, we demonstrate how this approach mitigates the challenge of power allocation in multi-cell networks. Our method reduces the search space for optimization, significantly decreasing computational overhea…

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna
Yike Wang, Zhike Wu, Jiang Chen, Chunjie Wang, Xuhui Zhang, Yanyan Shen
https://arxiv.org/abs/2508.07799

QoS-Aware Integrated Sensing, Communication, and Control with Movable Antenna
Integrated sensing, communication, and control (ISCC) has emerged as a key enabler for low-altitude wireless networks with enhanced adaptability through resource allocation co-design and intelligent environment awareness. However, dynamic interference and channel attenuation constrain the potential of the ISCC system. To address this challenge, we propose a novel movable antenna-empowered ISCC system. An achievable data rate maximization problem is formulated while guaranteeing the sensing and …

QTCAJOSA: Low-Complexity Joint Offloading and Subchannel Allocation for NTN-Enabled IoMT
Alejandro Flores C., Konstantinos Ntontin, Ashok Bandi, Symeon Chatzinotas
https://arxiv.org/abs/2507.13242

QTCAJOSA: Low-Complexity Joint Offloading and Subchannel Allocation for NTN-Enabled IoMT
In this work, we consider the resource allocation problem for task offloading from Internet of Medical Things (IoMT) devices, to a non-terrestrial network. The architecture considers clusters of IoMT devices that offload their tasks to a dedicated unmanned aerial vehicle (UAV) serving as a multi-access edge computing (MEC) server, which can compute the task or further offload it to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite for remote computing. W…

Resource-Splitting Games with Tullock-Based Lossy Contests
Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
https://arxiv.org/abs/2507.13853 https://…

Resource-Splitting Games with Tullock-Based Lossy Contests
This paper introduces a novel class of multi-stage resource allocation games that model real-world scenarios in which profitability depends on the balance between supply and demand, and where higher resource investment leads to greater returns. Our proposed framework, which incorporates the notion of profit loss due to insufficient player participation, gives rise to a Tullock-like functional form of the stage payoff structure when weighted fair proportional resource allocation is applied. We e…

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Gabriel Aracena, Kyle Luster, Fabio Santos, Igor Steinmacher, Marco A. Gerosa
https://arxiv.org/abs/2506.00128

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Effective prioritization of issue reports in software engineering helps to optimize resource allocation and information recovery. However, manual issue classification is laborious and lacks scalability. As an alternative, many open source software (OSS) projects employ automated processes for this task, yet this method often relies on large datasets for adequate training. Traditionally, machine learning techniques have been used for issue classification. More recently, large language models (LL…

Epidemic dynamics in physical-information-social multilayer networks
Mengshou Wang, Liangrong Peng, Baoguo Jia, Liu Hong
https://arxiv.org/abs/2506.00104 h…

Epidemic dynamics in physical-information-social multilayer networks
During epidemic outbreaks, information dissemination enhances individual protection, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in different scales by using the Microscopic Markov Chain Approach and mean-field approximations. The basic reproduction number and epidemic th…

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
Rodrigo Moreira, Larissa F. Rodrigues Moreira, Fl\'avio de Oliveira Silva
https://arxiv.org/abs/2507.17850

Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functi…

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?
Jia Guo, Chenyang Yang
https://arxiv.org/abs/2507.02427 https://…

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?
Owing to the use of attention mechanism to leverage the dependency across tokens, Transformers are efficient for natural language processing. By harnessing permutation properties broadly exist in resource allocation policies, each mapping measurable environmental parameters (e.g., channel matrix) to optimized variables (e.g., precoding matrix), graph neural networks (GNNs) are promising for learning these policies efficiently in terms of scalability and generalizability. To reap the benefits of…

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Lei Deng, Wenhan Xu, Jingwei Li, Danny H. K. Tsang
https://arxiv.org/abs/2506.09647

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic fo…

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
Gurusha Juneja, Alon Albalak, Wenyue Hua, William Yang Wang
https://arxiv.org/abs/2506.20737

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often access proprietary tools and domain-specific databases requiring strict confidentiality. This paper examines whether LLM-based agents demonstrate an understanding of contextual privacy. And, if instructed, do these systems preserve inference time user privacy in non-adversarial m…

Refined Metrics, Sensing Limits, and Resource Allocation in OTFS-RSMA LEO ISAC
Bruno Felipe Costa, Taufik Abr\~ao
https://arxiv.org/abs/2506.02624 https://…

Refined Metrics, Sensing Limits, and Resource Allocation in OTFS-RSMA LEO ISAC
This paper develops an integrated OTFS-RSMA framework employing advanced SP techniques tailored for this demanding environment. We derive refined communication performance metrics, specifically SINR expressions capturing the practical effects of ICSI and ISIC. Moreover, fundamental sensing limits are established via CRB derivation incorporating parameter-dependent echo gain, linking waveform SP properties to estimation accuracy. The resource allocation is formulated as a non-convex optimization…

Near-feasible Fair Allocations in Two-sided Markets
Javier Cembrano, Andr\'es Moraga, Victor Verdugo
https://arxiv.org/abs/2506.01178 https://

Near-feasible Fair Allocations in Two-sided Markets
We study resource allocation in two-sided markets from a fundamental perspective and introduce a general modeling and algorithmic framework to effectively incorporate the complex and multidimensional aspects of fairness. Our main technical contribution is to show the existence of a range of near-feasible resource allocations parameterized in different model primitives to give flexibility when balancing the different policymaking requirements, allowing policy designers to fix these values accord…

Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion
Yifan Xue, Ruihuai Liang, Bo Yang, Xuelin Cao, Zhiwen Yu, M\'erouane Debbah, Chau Yuen
https://arxiv.org/abs/2506.21933

Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion
With the rapid development of the low-altitude economy, air-ground integrated multi-access edge computing (MEC) systems are facing increasing demands for real-time and intelligent task scheduling. In such systems, task offloading and resource allocation encounter multiple challenges, including node heterogeneity, unstable communication links, and dynamic task variations. To address these issues, this paper constructs a three-layer heterogeneous MEC system architecture for low-altitude economic …

A distributed system perspective on Backscatter systems: A review
Tonghuan Xiao, Jiecheng Zhou
https://arxiv.org/abs/2506.04873 https://

A distributed system perspective on Backscatter systems: A review
This review investigates the pivotal role of distributed architectures and intelligent resource allocation in enabling robust and scalable wireless systems, with a particular emphasis on backscatter communication, indoor localization, battery-free networks, and Simultaneous Wireless Information and Power Transfer (SWIPT).

SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning
Vasavi Lankipalle
https://arxiv.org/abs/2507.00005 https://…

SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning
Disaster response requires rapid, adaptive decision-making in chaotic environments. SwarmFusion, a novel hybrid framework, integrates particle swarm optimization with convolutional neural networks to optimize real-time resource allocation and path planning. By processing live satellite, drone, and sensor data, SwarmFusion enhances situational awareness and operational efficiency in flood and wildfire scenarios. Simulations using the DisasterSim2025 dataset demonstrate up to 40 percentage faster…

Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks
Vasileios Kouvakis, Stylianos E. Trevlakis, Ioannis Arapakis, Alexandros-Apostolos A. Boulogeorgos
https://arxiv.org/abs/2508.03279

Spiking Neural Networks for Resource Allocation in UAV-Enabled Wireless Networks
This work presents a new spiking neural network (SNN)-based approach for user equipment-base station (UE-BS) association in non-terrestrial networks (NTNs). With the introduction of UAV's in wireless networks, the system architecture becomes heterogeneous, resulting in the need for dynamic and efficient management to avoid congestion and sustain overall performance. The presented framework compares two SNN-based optimization strategies. Specifically, a top-down centralized approach with complet…

This https://arxiv.org/abs/2411.12069 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Matroid Secretary via Labeling Schemes
The Matroid Secretary Problem (MSP) is one of the most prominent settings for online resource allocation and optimal stopping. A decision-maker is presented with a ground set of elements $E$ revealed sequentially and in random order. Upon arrival, an irrevocable decision is made in a take-it-or-leave-it fashion, subject to a feasibility constraint on the set of selected elements captured by a matroid defined over $E$. The decision-maker only has ordinal access to compare the elements, and the g…

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
Ahmet Melih Ince, Ayse Elif Canbilen, Halim Yanikomeroglu
https://arxiv.org/abs/2508.00011 h…

AoI-Aware Resource Allocation with Deep Reinforcement Learning for HAPS-V2X Networks
Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication rel…

Autonomous Resource Management in Microservice Systems via Reinforcement Learning
Yujun Zou, Nia Qi, Yingnan Deng, Zhihao Xue, Ming Gong, Wuyang Zhang
https://arxiv.org/abs/2507.12879

Autonomous Resource Management in Microservice Systems via Reinforcement Learning
This paper proposes a reinforcement learning-based method for microservice resource scheduling and optimization, aiming to address issues such as uneven resource allocation, high latency, and insufficient throughput in traditional microservice architectures. In microservice systems, as the number of services and the load increase, efficiently scheduling and allocating resources such as computing power, memory, and storage becomes a critical research challenge. To address this, the paper employs…

Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning
Saichao Liu, Geng Sun, Chuang Zhang, Xuejie Liu, Jiacheng Wang, Changyuan Zhao, Dusit Niyato
https://arxiv.org/abs/2508.00261

Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning
Mobile edge computing (MEC) is a promising technique to improve the computational capacity of smart devices (SDs) in Internet of Things (IoT). However, the performance of MEC is restricted due to its fixed location and limited service scope. Hence, we investigate an unmanned aerial vehicle (UAV)-assisted MEC system, where multiple UAVs are dispatched and each UAV can simultaneously provide computing service for multiple SDs. To improve the performance of system, we formulated a UAV-based trajec…

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
https://arxiv.org/abs/2506.18748

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous dual subgradient methods by representing the network configurations (or states) as graphs and viewing dual variables as dynamic inputs t…

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Jeroen Spaans, Jesse Heyninck
https://arxiv.org/abs/2507.16067

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowe…

Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems
Mehdi Zafari, Rang Liu, A. Lee Swindlehurst
https://arxiv.org/abs/2508.01044 https://

Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems
Integrated Sensing and Communication (ISAC) is emerging as a key enabler for 6G wireless networks, allowing the joint use of spectrum and infrastructure for both communication and sensing. While prior ISAC solutions have addressed resource optimization, including power allocation, beamforming, and waveform design, they often rely on centralized architectures with full network knowledge, limiting their scalability in distributed systems. In this paper, we propose two coordinated decentralized op…

Virne: A Comprehensive Benchmark for Deep RL-based Network Resource Allocation in NFV
Tianfu Wang, Liwei Deng, Xi Chen, Junyang Wang, Huiguo He, Leilei Ding, Wei Wu, Qilin Fan, Hui Xiong
https://arxiv.org/abs/2507.19234

Virne: A Comprehensive Benchmark for Deep RL-based Network Resource Allocation in NFV
Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. Recently, deep Reinforcement Learning (RL)-based methods have been showing promising potential to address this complexity. However, the lack of a systematic benchmarking framework and thorough analysis hinders the exploration of emerging networks and the development of more robust algorithms while causing inconsistent evaluation. In this paper, we …

Predictive Control over LAWN: Joint Trajectory Design and Resource Allocation
Haijia Jin, Jun Wu, Weijie Yuan, Ruizhi Ruan, Jiacheng Wang, Dusit Niyato, Dong In Kim, Abbas Jamalipour
https://arxiv.org/abs/2507.02374

Predictive Control over LAWN: Joint Trajectory Design and Resource Allocation
Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over a LAWN system, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory track…

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
Qiong Wu, Yu Xie, Pingyi Fan, Dong Qin, Kezhi Wang, Nan Cheng, Khaled B. Letaief
https://arxiv.org/abs/2507.19050

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context lear…

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI
Ming Zeng, Xianbin Wang, Yuanwei Liu, Zhiguo Ding, George K. Karagiannidis, H. Vincent Poor
https://arxiv.org/abs/2507.12582

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI
Pinching-antenna technology has lately showcased its promising capability for reconfiguring wireless propagation environments, especially in high-frequency communication systems like millimeter-wave and terahertz bands. By dynamically placing the antenna over a dielectric waveguide, line-of-sight (LoS) connections can be made to significantly improve system performance. Although recent research have illustrated the advantages of pinching-antenna-assisted designs, they mainly presuppose complete…

Online Rounding Schemes for $ k $-Rental Problems
Hossein Nekouyan, Bo Sun, Raouf Boutaba, Xiaoqi Tan
https://arxiv.org/abs/2507.19649 https://arxiv.org/pd…

Online Rounding Schemes for $ k $-Rental Problems
We study two online resource-allocation problems with reusability in an adversarial setting, namely kRental-Fixed and kRental-Variable. In both problems, a decision-maker manages $k$ identical reusable units and faces a sequence of rental requests over time. We develop theoretically grounded relax-and-round algorithms with provable competitive-ratio guarantees for both settings. For kRental-Fixed, we present an optimal randomized algorithm that attains the best possible competitive ratio: it fi…

Multibeam High Throughput Satellite: Hardware Foundation, Resource Allocation, and Precoding
Rui Chen, Wen-Xuan Long, Bing-Qian Wang, Yuan He, Rui-Jin Sun, Nan Cheng, Gan Zheng, Dusit Niyato
https://arxiv.org/abs/2508.00800

Multibeam High Throughput Satellite: Hardware Foundation, Resource Allocation, and Precoding
With its wide coverage and uninterrupted service, satellite communication is a critical technology for next-generation 6G communications. High throughput satellite (HTS) systems, utilizing multipoint beam and frequency multiplexing techniques, enable satellite communication capacity of up to Tbps to meet the growing traffic demand. Therefore, it is imperative to review the-state-of-the-art of multibeam HTS systems and identify their associated challenges and perspectives. Firstly, we summarize …

Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation
Shengjie Liu, Chenyang Yang
https://arxiv.org/abs/2506.21208

Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation
Deep neural networks (DNNs) have widespread applications for optimizing resource allocation. Yet, their performance is vulnerable to distribution shifts between training and test data, say channels. In this letter, we resort to adversarial training (AT) for enhancing out-of-distribution (OOD) generalizability of DNNs trained in unsupervised manner. We reformulate AT to capture the OOD degradation, and propose a one-step gradient ascent method for AT. The proposed method is validated by optimizi…

A Vision for Trustworthy, Fair, and Efficient Socio-Technical Control using Karma Economies
Ezzat Elokda, Andrea Censi, Emilio Frazzoli, Florian D\"orfler, Saverio Bolognani
https://arxiv.org/abs/2506.17115

A Vision for Trustworthy, Fair, and Efficient Socio-Technical Control using Karma Economies
Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc.. Historical evidence suggests that monetary control -- the prototypical mechanism for managing resource scarcity -- is not always well-accepted in socio-technical resource …

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
Eugene Armah, Linda Amoako Bannning
https://arxiv.org/abs/2507.14597

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
Processing data at high speeds is becoming increasingly critical as digital economies generate enormous data. The current paradigms for timely data processing are edge computing and data stream processing (DSP). Edge computing places resources closer to where data is generated, while stream processing analyzes the unbounded high-speed data in motion. However, edge stream processing faces rapid workload fluctuations, complicating resource provisioning. Inadequate resource allocation leads to bot…

RL-Driven Semantic Compression Model Selection and Resource Allocation in Semantic Communication Systems
Xinyi Lin, Peizheng Li, Adnan Aijaz
https://arxiv.org/abs/2506.18660

RL-Driven Semantic Compression Model Selection and Resource Allocation in Semantic Communication Systems
Semantic communication (SemCom) is an emerging paradigm that leverages semantic-level understanding to improve communication efficiency, particularly in resource-constrained scenarios. However, existing SemCom systems often overlook diverse computational and communication capabilities and requirements among different users. Motivated by the need to adaptively balance semantic accuracy, latency, and energy consumption, this paper presents a reinforcement learning (RL)-driven framework for semant…

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez, Syed Ali Zaidi, Hamed Ahmadi
https://arxiv.org/abs/2507.21698

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication ef…

The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking
Antonio Ojea
https://arxiv.org/abs/2506.23628 https://

The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking
Traditional Kubernetes networking struggles to meet the escalating demands of AI/ML and evolving Telco infrastructure. This paper introduces Kubernetes Network Drivers (KNDs), a transformative, modular, and declarative architecture designed to overcome current imperative provisioning and API limitations. KNDs integrate network resource management into Kubernetes' core by utilizing Dynamic Resource Allocation (DRA), Node Resource Interface (NRI) improvements, and upcoming OCI Runtime Specificati…

Energy-Aware Resource Allocation for Multi-Operator Cell-Free Massive MIMO in V-CRAN Architectures
Derya Nurcan-Atceken, \"Ozlem Tu\u{g}fe Demir, Aysegul Altin-Kayhan, Emil Bj\"ornson, Cicek Cavdar, Bulent Tavli
https://arxiv.org/abs/2507.21644

Energy-Aware Resource Allocation for Multi-Operator Cell-Free Massive MIMO in V-CRAN Architectures
Cell-free massive multiple-input multiple-output (MIMO) implemented in virtualized cloud radio access networks (V-CRAN) has emerged as a promising architecture to enhance spectral efficiency (SE), network flexibility, and energy efficiency (EE) in next-generation wireless systems. In this work, we develop a holistic optimization framework for the efficient deployment of cell-free massive MIMO in V-CRAN with multiple mobile network operators (MNOs). Specifically, we formulate a set of mixed-inte…

GPUnion: Autonomous GPU Sharing on Campus
Yufang Li (The Hong Kong University of Science,Technology), Yuanbo Zhang (Sun Yat-sen University), Hanlong Liao (National University of Defense Technology), Guoming Tang (The Hong Kong University of Science,Technology), Deke Guo (Sun Yat-sen University)
https://arxiv.org/abs/2507.18928

GPUnion: Autonomous GPU Sharing on Campus
A pronounced imbalance in GPU resources exists on campus, where some laboratories own underutilized servers while others lack the compute needed for AI research. GPU sharing can alleviate this disparity, while existing platforms typically rely on centralized oversight and persistent allocation models, conflicting with the voluntary and autonomous nature of academic resource ownership. We present GPUnion, a campus-scale GPU sharing platform enabling voluntary participation while preserving full …

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Majumder Haider, Imtiaz Ahmed, Zoheb Hassan, Kamrul Hasan, H. Vincent Poor
https://arxiv.org/abs/2506.18293

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual rea…

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
Jer Shyuan Ng, Aditya Pribadi Kalapaaking, Xiaoyu Xia, Dusit Niyato, Ibrahim Khalil, Iqbal Gondal
https://arxiv.org/abs/2506.18259

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
In recent years, Federated Learning (FL) has emerged as a widely adopted privacy-preserving distributed training approach, attracting significant interest from both academia and industry. Research efforts have been dedicated to improving different aspects of FL, such as algorithm improvement, resource allocation, and client selection, to enable its deployment in distributed edge networks for practical applications. One of the reasons for the poor FL model performance is due to the worker dropou…

This https://arxiv.org/abs/2501.16143 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Disruption-aware Microservice Re-orchestration for Cost-efficient Multi-cloud Deployments
Multi-cloud environments enable a cost-efficient scaling of cloud-native applications across geographically distributed virtual nodes with different pricing models. In this context, the resource fragmentation caused by frequent changes in the resource demands of deployed microservices, along with the allocation or termination of new and existing microservices, increases the deployment cost. Therefore, re-orchestrating deployed microservices on a cheaper configuration of multi-cloud nodes offers…

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Xiaoning Jia, Ruilin Kong, Guangya Si, Bilong Shen, Zhe Ji
https://arxiv.org/abs/2507.18050

A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation
Rising demand for complex simulations highlights conventional engines'scalability limits, spurring Parallel Discrete Event Simulation (PDES) adoption.Warped2, a PDES engine leveraging Time Warp synchronization with Pending Event Set optimization, delivers strong performance, it struggles with inherent wargaming limitations: inefficient LP resource allocation during synchronization and unaddressed complex entity interaction patterns. To address these challenges, we present an optimized framework…

Beam-Hopping Pattern Design for Grant-Free Random Access in LEO Satellite Communications
Seunghyeon Jeon, Seonjung Kim, Gyeongrae Im, Yo-Seb Jeon
https://arxiv.org/abs/2508.03391

Beam-Hopping Pattern Design for Grant-Free Random Access in LEO Satellite Communications
Increasing demand for massive device connectivity in underserved regions drives the development of advanced low Earth orbit (LEO) satellite communication systems. Beam-hopping LEO systems without connection establishment provide a promising solution for achieving both demand-aware resource allocation and low access latency. This paper investigates beam-hopping pattern design for the grant-free random access systems to dynamically allocate satellite resources according to traffic demands across …

Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G
Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi, Halim Yanikomeroglu
https://arxiv.org/abs/2507.14310

Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G
This paper proposes an innovative approach by leveraging uncrewed aerial vehicles (UAVs) as base stations (BSs) and a high-altitude platform station (HAPS) as the central processing unit (CPU) in an integrated sensing and communication (ISAC) system for 6G networks. We explore the challenges, applications, and advantages of ISAC systems in next-generation networks, highlighting the significance of optimizing position and power control. Our approach integrates HAPS and UAVs to enhance wireless c…

OpenRASE: Service Function Chain Emulation
Theviyanthan Krishnamohan, Paul Harvey
https://arxiv.org/abs/2507.22131 https://arxiv.org/pdf/2507.22131

OpenRASE: Service Function Chain Emulation
Service Function Chains (SFCs) are one of the key enablers in providing programmable computer networks, paving the way for network autonomy. However, this also introduces new challenges, such as resource allocation and optimisation related to their operation, requiring new algorithms to address these challenges. Various tools have been used in the literature to evaluate these algorithms. However, these tools suffer from inaccuracy, low fidelity, unscalability, inflexibility, or additional code …

Delay Bound Relaxation with Deep Learning-based Haptic Estimation for Tactile Internet
Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang
https://arxiv.org/abs/2507.00571

Delay Bound Relaxation with Deep Learning-based Haptic Estimation for Tactile Internet
Haptic teleoperation typically demands sub-millisecond latency and ultra-high reliability (99.999%) in Tactile Internet. At a 1 kHz haptic signal sampling rate, this translates into an extremely high packet transmission rate, posing significant challenges for timely delivery and introducing substantial complexity and overhead in radio resource allocation. To address this critical challenge, we introduce a novel DL modelthat estimates force feedback using multi-modal input, i.e. both force measu…

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Anup Mishra, \v{C}edomir Stefanovi\'c, Xiuqiang Xu, Petar Popovski, Israel Leyva-Mayorga
https://arxiv.org/abs/2506.15273

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Flexible and efficient wireless resource sharing across heterogeneous services is a key objective for future wireless networks. In this context, we investigate the performance of a system where latency-constrained internet-of-things (IoT) devices coexist with a broadband user. The base station adopts a grant-free access framework to manage resource allocation, either through orthogonal radio access network (RAN) slicing or by allowing shared access between services. For the IoT users, we propos…