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CGSim: A Simulation Framework for Large Scale Distributed Computing Environment
Sairam Sri Vatsavai, Raees Khan, Kuan-Chieh Hsu, Ozgur O. Kilic, Paul Nilsson, Tatiana Korchuganova, David K. Park, Sankha Dutta, Yihui Ren, Joseph Boudreau, Tasnuva Chowdhury, Shengyu Feng, Jaehyung Kim, Scott Klasky, Tadashi Maeno, Verena Ingrid Martinez, Norbert Podhorszki, Fr\'ed\'eric Suter, Wei Yang, Yiming Yang, Shinjae Yoo, Alexei Klimentov, Adolfy Hoisie

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment
Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distribu…

A Framework for Distributed Resource Allocation in Quantum Networks
Nitish K. Panigrahy, Leonardo Bacciottini, C. V. Hollot, Emily A. Van Milligen, Matheus Guedes de Andrade, Nageswara S. V. Rao, Gayane Vardoyan, Don Towsley
https://arxiv.org/abs/2510.09371

A Framework for Distributed Resource Allocation in Quantum Networks
We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms under the mathematical framework of Quantum Network Utility Maximization (QNUM), where utility functions quantify network performance by mapping entanglement rate and quality into a joint optimization objective. We then introduce QPrimal-Dual, a decentralized, sc…

Online Multiple Resource Allocation Problems with Departures via the Primal-Dual Approach
Yusuf Amidu, Khaled Elbassioni, Adriana F. Gabor
https://arxiv.org/abs/2510.04737 https…

Online Multiple Resource Allocation Problems with Departures via the Primal-Dual Approach
In this paper we propose primal-dual algorithms for different variants of the online resource allocation problem with departures. In the basic variant, requests (items) arrive over time to a set of resources (knapsacks) and upon arrival, the duration of time a request may occupy a resource, the demand and reward if the request can be granted, become known. %We assume that the duration of stay of a request may depend on the resource. %and that resources may have different capacity sizes. The goa…

Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Jinho Cha, Justin Yoo, Eunchan Daniel Cha, Emily Yoo, Caedon Geoffrey, Hyoshin Song
https://arxiv.org/abs/2510.05504

Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending c…

Auctioning Future Services in Edge Networks with Moving Vehicles: N-Step Look-Ahead Contracts for Sustainable Resource Provision
Ziqi Ling, Minghui Liwang, Xianbin Wang, Seyyedali Hosseinalipour, Zhipeng Cheng, Sai Zou, Wei Ni, Xiaoyu Xia
https://arxiv.org/abs/2510.07333

Auctioning Future Services in Edge Networks with Moving Vehicles: N-Step Look-Ahead Contracts for Sustainable Resource Provision
Timely resource allocation in edge-assisted vehicular networks is essential for compute-intensive services such as autonomous driving and navigation. However, vehicle mobility leads to spatio-temporal unpredictability of resource demands, while real-time double auctions incur significant latency. To address these challenges, we propose a look-ahead contract-based auction framework that shifts decision-making from runtime to planning time. Our approach establishes N-step service contracts betwee…

Online Allocation with Concave, Diminishing-Returns Objectives
Kalen Patton
https://arxiv.org/abs/2510.11266 https://arxiv.org/pdf/2510.11266

Online Allocation with Concave, Diminishing-Returns Objectives
Online resource allocation problems are central challenges in economics and computer science, modeling situations in which $n$ items arriving one at a time must each be immediately allocated among $m$ agents. In such problems, our objective is to maximize a monotone reward function $f(\mathbf{x})$ over the allocation vector $\mathbf{x} = (x_{ij})_{i, j}$, which describes the amount of each item given to each agent. In settings where $f$ is concave and has "diminishing returns" (monotone decreas…

CPU-Limits kill Performance: Time to rethink Resource Control
Chirag Shetty, Sarthak Chakraborty, Hubertus Franke, Larisa Shwartz, Chandra Narayanaswami, Indranil Gupta, Saurabh Jha
https://arxiv.org/abs/2510.10747

CPU-Limits kill Performance: Time to rethink Resource Control
Research in compute resource management for cloud-native applications is dominated by the problem of setting optimal CPU limits -- a fundamental OS mechanism that strictly restricts a container's CPU usage to its specified CPU-limits . Rightsizing and autoscaling works have innovated on allocation/scaling policies assuming the ubiquity and necessity of CPU-limits . We question this. Practical experiences of cloud users indicate that CPU-limits harms application performance and costs more than i…

Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation
Rabiya Khalid, Evangelos Pournaras
https://arxiv.org/abs/2510.04192 https://

Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation
The growing electricity demand and increased use of smart appliances are placing new pressures on power grids, making efficient energy management more important than ever. The existing energy management systems often prioritize system efficiency (balanced energy demand and supply) at the expense of user comfort. This paper addresses this gap by proposing a novel decentralized multi-agent coordination-based demand-side management system. The proposed system enables individual agents to coordinat…

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Yuanjun Dai, Keqiang He, An Wang
https://arxiv.org/abs/2510.08522 https://

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Existing batch size selection approaches in dis- tributed machine learning rely on static allocation or simplistic heuristics that fail to adapt to heterogeneous, dynamic computing environments. We present DYNAMIX, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO). Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource uti…

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Yu-Zhe Shi, Qiao Xu, Yanjia Li, Mingchen Liu, Huamin Qu, Lecheng Ruan, Qining Wang
https://arxiv.org/abs/2510.02679

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, part…

Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices
Proggya Chakraborty, Aaquib Asrar, Jayasree Sengupta, Sipra Das Bit
https://arxiv.org/abs/2510.08769

Prioritizing Latency with Profit: A DRL-Based Admission Control for 5G Network Slices
5G networks enable diverse services such as eMBB, URLLC, and mMTC through network slicing, necessitating intelligent admission control and resource allocation to meet stringent QoS requirements while maximizing Network Service Provider (NSP) profits. However, existing Deep Reinforcement Learning (DRL) frameworks focus primarily on profit optimization without explicitly accounting for service delay, potentially leading to QoS violations for latency-sensitive slices. Moreover, commonly used epsil…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Harshit Goyal
https://arxiv.org/abs/2510.05127 https://arxiv.org/pdf/2510.…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Efficient resource allocation is a key challenge in modern cloud computing. Over-provisioning leads to unnecessary costs, while under-provisioning risks performance degradation and SLA violations. This work presents an artificial intelligence approach to predict resource utilization in big data pipelines using Random Forest regression. We preprocess the Google Borg cluster traces to clean, transform, and extract relevant features (CPU, memory, usage distributions). The model achieves high predi…

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
Quentin Renau, Amjad Ullah, Emma Hart
https://arxiv.org/abs/2510.08228 https://arxiv.…

Distributed Resource Selection for Self-Organising Cloud-Edge Systems
This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distribu…

AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models
Bo Ma, Hang Li, ZeHua Hu, XiaoFan Gui, LuYao Liu, Simon Liu
https://arxiv.org/abs/2510.02669 ht…

AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models
Multi-agent systems powered by large language models have demonstrated remarkable capabilities across diverse domains, yet existing automated design approaches seek monolithic solutions that fail to adapt resource allocation based on query complexity and domain requirements. This paper introduces AutoMaAS, a self-evolving multi-agent architecture search framework that leverages neural architecture search principles to automatically discover optimal agent configurations through dynamic operator …

Erdos-Turan photonic Ising machines with record-high coupling resolution
Huaqiang Li, Guangfeng Wang, Erez Hasman, Bo Wang, Xianfeng Chen
https://arxiv.org/abs/2510.01998 https:…

Erdos-Turan photonic Ising machines with record-high coupling resolution
Ising machines have emerged as promising platforms for efficiently tackling a wide range of combinatorial optimization problems relevant to resource allocation, statistical inference and deep learning, yet their practical utility is fundamentally constrained by the coarse resolution of spin-spin couplings (Jij). Current implementations, relying on direct modulation of physical parameters, achieve at most 256 discrete coupling levels, which severely hinder the faithfully modeling of arbitrary re…

Crosslisted article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- Mechanism design and equilibrium analysis of smart contract mediated resource allocation
Jinho Cha, Justin Yoo, Eunchan Daniel Cha, Emily Yoo, Caedon Geoffrey, Hyoshin Song

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Distributed Feedback-Feedforward Algorithms for Time-Varying Resource Allocation
Yiqiao Xu, Tengyang Gong, Zhengtao Ding, Alessandra Parisio

Evaluating UORA-Based Polling Mechanism for Latency-Sensitive Uplink Traffic in Wi-Fi Networks
Douglas Dziedzorm Agbeve, Andrey Belogaev, Chris Blondia, Jeroen Famaey
https://arxiv.org/abs/2510.04731

Evaluating UORA-Based Polling Mechanism for Latency-Sensitive Uplink Traffic in Wi-Fi Networks
IEEE 802.11ax (Wi-Fi 6) introduced Orthogonal Frequency Division Multiple Access (OFDMA), which enables simultaneous transmissions through centralized resource allocation. However, effective uplink scheduling requires the Access Point (AP) to identify which stations (STAs) have data to transmit. This typically necessitates polling for buffer status reports, a process that becomes increasingly inefficient and unscalable with growing device density. In this paper, we study how the Uplink OFDMA-ba…

The Online Submodular Cover Problem
Anupam Gupta, Roie Levin
https://arxiv.org/abs/2510.08883 https://arxiv.org/pdf/2510.08883

The Online Submodular Cover Problem
In the submodular cover problem, we are given a monotone submodular function $f$, and we want to pick the min-cost set $S$ such that $f(S) = f(N)$. Motivated by problems in network monitoring and resource allocation, we consider the submodular cover problem in an online setting. As a concrete example, suppose at each time $t$, a nonnegative monotone submodular function $g_t$ is given to us. We define $f^{(t)} = \sum_{s \leq t} g_s$ as the sum of all functions seen so far. We need to maintain a …

On Dynamic Programming Theory for Leader-Follower Stochastic Games
Jilles Steeve Dibangoye, Thibaut Le Marre, Ocan Sankur, Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2512.05667 https://arxiv.org/pdf/2512.05667 https://arxiv.org/html/2512.05667
arXiv:2512.05667v1 Announce Type: new
Abstract: Leader-follower general-sum stochastic games (LF-GSSGs) model sequential decision-making under asymmetric commitment, where a leader commits to a policy and a follower best responds, yielding a strong Stackelberg equilibrium (SSE) with leader-favourable tie-breaking. This paper introduces a dynamic programming (DP) framework that applies Bellman recursion over credible sets-state abstractions formally representing all rational follower best responses under partial leader commitments-to compute SSEs. We first prove that any LF-GSSG admits a lossless reduction to a Markov decision process (MDP) over credible sets. We further establish that synthesising an optimal memoryless deterministic leader policy is NP-hard, motivating the development of {\epsilon}-optimal DP algorithms with provable guarantees on leader exploitability. Experiments on standard mixed-motive benchmarks-including security games, resource allocation, and adversarial planning-demonstrate empirical gains in leader value and runtime scalability over state-of-the-art methods.
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Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting
Kassahun Azezew, Amsalu Tesema, Bitew Mekuria, Ayenew Kassie, Animut Embiale, Ayodeji Olalekan Salau, Tsega Asresa
https://arxiv.org/abs/2510.01302

Hybrid Predictive Modeling of Malaria Incidence in the Amhara Region, Ethiopia: Integrating Multi-Output Regression and Time-Series Forecasting
Malaria remains a major public health concern in Ethiopia, particularly in the Amhara Region, where seasonal and unpredictable transmission patterns make prevention and control challenging. Accurately forecasting malaria outbreaks is essential for effective resource allocation and timely interventions. This study proposes a hybrid predictive modeling framework that combines time-series forecasting, multi-output regression, and conventional regression-based prediction to forecast the incidence o…

Towards Energy-Efficient Serverless Computing with Hardware Isolation
Natalie Carl, Tobias Pfandzelter, David Bermbach
https://arxiv.org/abs/2510.08180 https://

Towards Energy-Efficient Serverless Computing with Hardware Isolation
Serverless computing provides just-in-time infrastructure provisioning with rapid elasticity and a finely-grained pricing model. As full control of resource allocation is in the hands of the cloud provider and applications only consume resources when they actually perform work, we believe that serverless computing is uniquely positioned to maximize energy efficiency. However, the focus of current serverless platforms is to run hundreds or thousands of serverless functions from different tenan…

FlashResearch: Real-time Agent Orchestration for Efficient Deep Research
Lunyiu Nie, Nedim Lipka, Ryan A. Rossi, Swarat Chaudhuri
https://arxiv.org/abs/2510.05145 https://

FlashResearch: Real-time Agent Orchestration for Efficient Deep Research
Deep research agents, which synthesize information across diverse sources, are significantly constrained by their sequential reasoning processes. This architectural bottleneck results in high latency, poor runtime adaptability, and inefficient resource allocation, making them impractical for interactive applications. To overcome this, we introduce FlashResearch, a novel framework for efficient deep research that transforms sequential processing into parallel, runtime orchestration by dynamicall…