Optimizing data analytics workflow scheduling in cloud–fog computing with GNNs

doi:10.1016/j.future.2026.108703

Optimizing data analytics workflow scheduling in cloud–fog computing with GNNs

2026-07-10

0

PRE

AI

W

Waralak Chongdarakul

K

Khomkrit Yongcharoenchaiyasit

N

Nattapol Aunsri

S

Sujitra Arwatchananukul *

P

Prasitthichai Naronglerdrit *

DOI：10.1016/j.future.2026.108703

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Abstract

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• Presents a GraphSAGE scheduler for adaptive workflow selection in cloud-fog systems. • Uses GraphSAGE to classify workflow graphs and select the best scheduling heuristic. • Improves makespan, SLR, speedup, and throughput across benchmark datasets. • Combines machine learning and heuristics for scalable cloud-fog scheduling. • Shows reliable FogWorkflowSim performance across diverse VM configurations.

Optimizing data analytics workflow scheduling in cloud–fog computing with GNNs

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