This research agenda argues that cloud-native architectures, microservices, autoscaling, and emerging trends like serverless inference and federated learning are required to make large language models efficient and scalable.
DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-Based Clusters.IEEE Transactions on Services Computing.2024;17(6):3473-3484
2 Pith papers cite this work. Polarity classification is still indexing.
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A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
This research agenda argues that cloud-native architectures, microservices, autoscaling, and emerging trends like serverless inference and federated learning are required to make large language models efficient and scalable.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.