arXiv Machine Learning
AID: A Framework for AI Infrastructure Dynamics
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ARGOS: Reinforcement Learning-Driven Multidimensional Elasticity for Service Orchestration in the Computing Continuum
arXiv:2609.37085v1 Announce Type: cross Abstract: Data-intensive services in the Computing Continuum must balance analytics quality, resource usage, and cost across heterogeneous nodes with limited a...
arXiv Machine Learning
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Grounding Large Language Models as Generalizable Policies in Network Control
arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.
arXiv Machine Learning
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Design and Scheduling of an AI-based Queueing System
arXiv:2406. 06855v3 Announce Type: replace-cross Abstract: To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs.
arXiv AI
Oct 15
OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
OPTIC-ER is a reinforcement learning framework designed to improve emergency response in underserved African communities by providing real‑time, adaptive, and equitable dispatch decisions. It employs an attention‑guided actor‑critic architecture with a Context‑Rich State Vector and a Precision Reward Function, trained on high‑fidelity simulations using real data from Rivers State, Nigeria. In tests on 500 unseen incidents, OPTIC-ER reduced response times by over 35% and surpassed the nearest‑facility heuristic’s 62.94% optimal‑selection rate, while also producing infrastructure deficiency maps and equity monitoring dashboards.
arXiv Machine Learning
Sep 14
MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling
MCRL2 is a reinforcement learning framework that enhances microservice scheduling in cloud data centers by integrating multi-resource cross-attention-based representation learning. It introduces MCRL, a representation learning component that captures structured interactions among nodes, resources, and microservices, and couples this with an actor‑critic architecture and a maximum entropy objective. Experiments on real production cluster traces show that MCRL2 outperforms existing baselines in load balancing, scheduling success rate, and average completion time across diverse workloads.
arXiv AI
Jun 3
Brief Announcement: Generative Markov Model for Distributed Computing Systems
arXiv:2606. 03061v1 Announce Type: cross Abstract: Emerging distributed computing paradigms, such as the computing continuum, are inherently heterogeneous, stochastic, and complex.