A Modified Edge Cloud Framework for Reproducible Latency Evaluation of Diabetes Prediction Using FogBus2 and XGBoost
DOI:
https://doi.org/10.68307/crnh0c22Keywords:
edge computing, fog computing, FogBus2, XGBoost, diabetes prediction, , task offloading, latency measurementAbstract
Edge and fog computing can reduce communication delay for distributed healthcare analytics, but latency claims require controlled measurement. This paper specifies a three-tier framework in which the same trained XGBoost classifier and identical records are deployed in cloud-only, original FogBus2, and modified FogBus2 configurations. The proposed cost-based scheduler selects fog or cloud execution using round-trip time, CPU utilization, queue length, and bandwidth. On the 768-record Pima benchmark, leakage-safe evaluation produced 79.2% holdout accuracy, 68.0% F1-score, and 0.890 ROC-AUC; repeated stratified cross-validation produced 76.5% ± 3.3% accuracy and 0.840 ± 0.020 ROC-AUC. Earlier system-latency averages remain pilot observations pending repeated multi-device measurements. The Pima data are used only as a static benchmark workload, and no clinical-validation claim is made.
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