Resource Management
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Related Articles from SNS
Libra: Efficient Resource Management for Agentic RL Post-Training
arXiv:2606.03077v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors. In agentic RL, the rollout stage generates trajectories while invoking tools, producing long-tailed and non-stationary workloads that challenge conventional resource-management assumptions. Three fundamental challenges arise.
Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks
Announce Type: new Abstract: The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensuring reliable connectivity for ground users deployment. However, efficiently managing spectrum and resources in such highly dynamic UAV-assisted environments remains a major challenge due to nonlinear system interactions, mobility-induced topology...
Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management
arXiv:2606.05526v1 Announce Type: new Abstract: In this paper, multi-target tracking and scanning are considered in a radar system operating in the track-while-scan mode. Specifically, time allocation for radar scanning and tracking of multiple maneuvering targets under a time budget constraint is addressed, aiming to jointly optimize the performance of both tracking and scanning in a cognitive radar. We first present the details of the model for tracking and scanning and formulate the time...
Addressing Negative Commons Governance with Positive Commons Principles
arXiv:2606.04563v1 Announce Type: new Abstract: Computing is accompanied by both positive and negative commons throughout its lifecycle of creation, execution, and disposal. We examine two governance systems situated within this lifecycle -- global e-waste trade and the Linux kernel community -- to evaluate whether Elinor Ostrom's eight design principles for common-pool resource (CPR) governance extend to the management of negative common-pool resources (NCPRs).
The Ü Programming Language
Ü is a statically-typed compiled programming language, designed for writing programs, which should be both reliable and fast. It has safe and unsafe code separation, compile-time correctness checks, powerful abstractions like RAII and templates, encapsulation, rich type system, lambdas, coroutines and many other useful features. Ü uses RAII for memory and resources management (no GC is involved), but manual memory management may be still used in unsafe code.
HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster
arXiv:2605.31023v1 Announce Type: new Abstract: This work addresses the problem of autonomous resource management in heterogeneous satellite cluster conducting Earth Observation (EO) missions including optical and Synthetic Aperture Radar (SAR) satellites. In autonomous operation mode, satellites are equipped with intelligent capabilities enabling real-time decision-making based on the latest conditions, while requiring minimal interaction with ground operators. Traditional scheduling...
Toka: A Systems Programming Language with Explicit Resource Semantics
Announce Type: new Abstract: Systems programming languages traditionally struggle with the tension between physical transparency and compile-time memory safety. C++ provides direct, zero-cost hardware access but lacks strict safety boundaries, whereas Rust guarantees safety at the cost of complex lifetime annotations and implicit dereferencing chains. In this paper, we present Toka, a native systems programming language that establishes physical transparency in resource management via...
Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions
arXiv:2606.07046v1 Announce Type: new Abstract: Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions...
Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification
Announce Type: replace Abstract: Land Use Scene Classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have significantly advanced the state of the art, with Convolutional Neural Networks (CNNs) dominating the field because of their strong ability to capture local spatial features. However, the emergence of Vision Transformers (ViTs) has introduced a new...
Agricultural Landscape Understanding At Country-Scale
Announce Type: replace Abstract: Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management. This requires mapping not just crop fields, but also vital features like trees and water bodies which form an intricate mosaic in complex \textit{smallholder} systems dominating the Global South. Previous efforts to develop such land use maps have been limited by a narrow focus on methods for field...