Saved in:
| Main Authors: | Hamzeh, Hamed, Vahdatian, Parisa |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.07607 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
by: Hamzeh, Hamed
Published: (2026)
by: Hamzeh, Hamed
Published: (2026)
SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning
by: Su, Jianchang, et al.
Published: (2026)
by: Su, Jianchang, et al.
Published: (2026)
A Holistic Framework for Automated Configuration Recommendation for Cloud Service Monitoring
by: Bastos, Anson, et al.
Published: (2026)
by: Bastos, Anson, et al.
Published: (2026)
Mitigating Temporal Blindness in Kubernetes Autoscaling: An Attention-Double-LSTM Framework
by: Shaikh, Faraz, et al.
Published: (2026)
by: Shaikh, Faraz, et al.
Published: (2026)
From Models to Operators: Rethinking Autoscaling Granularity for Large Generative Models
by: Cui, Xingqi, et al.
Published: (2025)
by: Cui, Xingqi, et al.
Published: (2025)
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
by: Lu, Yao, et al.
Published: (2024)
by: Lu, Yao, et al.
Published: (2024)
Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods
by: Sedlak, Boris, et al.
Published: (2025)
by: Sedlak, Boris, et al.
Published: (2025)
A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exporter
by: Choochotkaew, Sunyanan, et al.
Published: (2024)
by: Choochotkaew, Sunyanan, et al.
Published: (2024)
HGraphScale: Hierarchical Graph Learning for Autoscaling Microservice Applications in Container-based Cloud Computing
by: Fang, Zhengxin, et al.
Published: (2025)
by: Fang, Zhengxin, et al.
Published: (2025)
Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices
by: Sedlak, Boris, et al.
Published: (2025)
by: Sedlak, Boris, et al.
Published: (2025)
MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters
by: Moore, H., et al.
Published: (2026)
by: Moore, H., et al.
Published: (2026)
Multi-Objective Optimization of Consumer Group Autoscaling in Message Broker Systems
by: Landau, Diogo, et al.
Published: (2024)
by: Landau, Diogo, et al.
Published: (2024)
Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings
by: Yuan, Liangqi, et al.
Published: (2025)
by: Yuan, Liangqi, et al.
Published: (2025)
Dependency Aware Incident Linking in Large Cloud Systems
by: Ghosh, Supriyo, et al.
Published: (2024)
by: Ghosh, Supriyo, et al.
Published: (2024)
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification
by: Jalali, Hamed, et al.
Published: (2022)
by: Jalali, Hamed, et al.
Published: (2022)
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
by: Li, Yanli, et al.
Published: (2024)
by: Li, Yanli, et al.
Published: (2024)
H-EYE: Holistic Resource Modeling and Management for Diversely Scaled Edge-Cloud Systems
by: Dagli, Ismet, et al.
Published: (2024)
by: Dagli, Ismet, et al.
Published: (2024)
A Comprehensive Forecasting Framework based on Multi-Stage Hierarchical Forecasting Reconciliation and Adjustment
by: Yang, Zhengchao, et al.
Published: (2024)
by: Yang, Zhengchao, et al.
Published: (2024)
A Theory of Multi-Agent Generative Flow Networks
by: Brunswic, Leo Maxime, et al.
Published: (2025)
by: Brunswic, Leo Maxime, et al.
Published: (2025)
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
by: Won, William, et al.
Published: (2023)
by: Won, William, et al.
Published: (2023)
A Learning-Based Caching Mechanism for Edge Content Delivery
by: Torabi, Hoda, et al.
Published: (2024)
by: Torabi, Hoda, et al.
Published: (2024)
FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel
by: Yan, Ran, et al.
Published: (2025)
by: Yan, Ran, et al.
Published: (2025)
Leveraging Neural Graph Compilers in Machine Learning Research for Edge-Cloud Systems
by: Furutanpey, Alireza, et al.
Published: (2025)
by: Furutanpey, Alireza, et al.
Published: (2025)
TorchGT: A Holistic System for Large-scale Graph Transformer Training
by: Zhang, Meng, et al.
Published: (2024)
by: Zhang, Meng, et al.
Published: (2024)
Hierarchical Autoscaling for Large Language Model Serving with Chiron
by: Patke, Archit, et al.
Published: (2025)
by: Patke, Archit, et al.
Published: (2025)
BatchWeave: A Consistent Object-Store-Native Data Plane for Large Foundation Model Training
by: Sun, Ting, et al.
Published: (2026)
by: Sun, Ting, et al.
Published: (2026)
FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems
by: Nguyen, Van-Dinh, et al.
Published: (2021)
by: Nguyen, Van-Dinh, et al.
Published: (2021)
CASA: A Framework for SLO and Carbon-Aware Autoscaling and Scheduling in Serverless Cloud Computing
by: Qi, S., et al.
Published: (2024)
by: Qi, S., et al.
Published: (2024)
PithTrain: A Compact and Agent-Native MoE Training System
by: Lai, Ruihang, et al.
Published: (2026)
by: Lai, Ruihang, et al.
Published: (2026)
AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
by: Pham, Dzung, et al.
Published: (2026)
by: Pham, Dzung, et al.
Published: (2026)
Cloud Native System for LLM Inference Serving
by: Xu, Minxian, et al.
Published: (2025)
by: Xu, Minxian, et al.
Published: (2025)
AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
by: Wang, Zhiyu, et al.
Published: (2025)
by: Wang, Zhiyu, et al.
Published: (2025)
Self-adaptive, Requirements-driven Autoscaling of Microservices
by: Nunes, João Paulo Karol Santos, et al.
Published: (2024)
by: Nunes, João Paulo Karol Santos, et al.
Published: (2024)
Proactive and Reactive Autoscaling Techniques for Edge Computing
by: Gupta, Suhrid, et al.
Published: (2025)
by: Gupta, Suhrid, et al.
Published: (2025)
LA-IMR: Latency-Aware, Predictive In-Memory Routing and Proactive Autoscaling for Tail-Latency-Sensitive Cloud Robotics
by: Seo, Eunil, et al.
Published: (2025)
by: Seo, Eunil, et al.
Published: (2025)
MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions
by: Ruilova, Federico, et al.
Published: (2025)
by: Ruilova, Federico, et al.
Published: (2025)
Governing Cloud Data Pipelines with Agentic AI
by: Kirubakaran, Aswathnarayan Muthukrishnan, et al.
Published: (2025)
by: Kirubakaran, Aswathnarayan Muthukrishnan, et al.
Published: (2025)
Improvements & Evaluations on the MLCommons CloudMask Benchmark
by: Chennamsetti, Varshitha, et al.
Published: (2024)
by: Chennamsetti, Varshitha, et al.
Published: (2024)
Towards an Adaptive Runtime System for Cloud-Native HPC
by: Bhosale, Aditya, et al.
Published: (2026)
by: Bhosale, Aditya, et al.
Published: (2026)
QAISim: A Toolkit for Modeling and Simulation of AI in Quantum Cloud Computing Environments
by: Singh, Irwindeep, et al.
Published: (2025)
by: Singh, Irwindeep, et al.
Published: (2025)
Similar Items
-
AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
by: Hamzeh, Hamed
Published: (2026) -
SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning
by: Su, Jianchang, et al.
Published: (2026) -
A Holistic Framework for Automated Configuration Recommendation for Cloud Service Monitoring
by: Bastos, Anson, et al.
Published: (2026) -
Mitigating Temporal Blindness in Kubernetes Autoscaling: An Attention-Double-LSTM Framework
by: Shaikh, Faraz, et al.
Published: (2026) -
From Models to Operators: Rethinking Autoscaling Granularity for Large Generative Models
by: Cui, Xingqi, et al.
Published: (2025)