arXiv Machine Learning
About the Influence of Workflow Topology on Task Intensity Prediction through Graph Learning
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arXiv AI
Sep 7
GLOW: Graph-Language Co-Encoding for Agentic Workflow Performance Prediction
GLOW is a framework that predicts the performance of Agentic Workflows by combining Graph Neural Networks with a graph-oriented Large Language Model. It extracts topology-aware semantic representations from workflow descriptions and fuses them with structural representations via a Transformer-based module, using contrastive learning to enhance discriminative power. Experiments on the FLORA-Bench benchmark show GLOW surpasses existing baselines in accuracy and ranking, and when used in the AFLOW generation system, it cuts optimization time by 98.7% with minimal loss in score.
arXiv AI
Jun 12
CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
arXiv:2606. 13513v1 Announce Type: new Abstract: Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels.
arXiv AI
Jun 2
Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
arXiv:2606. 01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources.
arXiv AI
Jul 29
Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations
arXiv:2607. 24773v1 Announce Type: new Abstract: Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical resources to minimize costs and maximize performance.
arXiv Machine Learning
Sep 15
Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning
arXiv:2609.14952v1 Announce Type: new Abstract: Dynamic cloud workflow scheduling must balance deadline satisfaction, container utilization, and energy consumption while dealing with stochastic task-...
arXiv AI
Sep 17
Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum
The paper introduces agentic-eCAL, an extension of the Energy Cost of AI Lifecycle metric to evaluate multi‑agent AI workflows across the edge‑cloud continuum. By combining a two‑rate energy model with OSI‑layer transport analysis, the authors quantify that inter‑agent text transfer accounts for only 0.25% of total workflow energy, highlighting that the main energy cost lies in additional inference and context processing triggered by communication. The study uses extensive GPU benchmarks on NVIDIA A100/H100 with 16 open‑weight models and 8 orchestration topologies to validate the metric and explore placement implications.