TY  - EJOU
AU  - Jian, Chuanlin 
AU  - Sun, Yuanchen 
AU  - Liu, Xiangcheng 
AU  - Huang, Xuming 
AU  - Kashi, Samaneh Beheshti 
AU  - Hu, Xing 

TI  - CASH: Confidence-Calibrated Deep Feature Crossing for Classification-Aware Heterogeneous Task Scheduling
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a multi-view workload representation from resource requests, submission context, queue attributes, and historical user behavior. A Deep & Cross Network captures bounded-degree high-order feature interactions; an eXtreme Gradient Boosting (XGBoost) branch models the sharp decision boundaries typical of tabular scheduling logs. A validation-calibrated, confidence-aware fusion rule combines the two branches into task-type probabilities with attached uncertainty estimates. The scheduler then performs class-constrained candidate selection and chooses execution nodes by minimizing a composite score over predicted execution time, energy, load imbalance, semantic mismatch, and prediction uncertainty. In trace-driven simulations on Massachusetts Institute of Technology (MIT) Supercloud logs, CASH improves resource matching, response time, makespan, and energy consumption over representative baselines while keeping load balance competitive. The results indicate that an explicit, calibrated link between workload semantics and hardware capability is a practical basis for efficient and interpretable cluster scheduling.
KW  - Heterogeneous computing; task scheduling; workload classification; deep & cross network; XGBoost; ensemble learning; uncertainty calibration; energy-aware scheduling

DO  - 10.32604/cmc.2026.086441