TFR-GNN: Topology- and Fault-Aware Graph Neural Scheduling for Heterogeneous Distributed Computing Systems

RESUMO

Scheduling workflow directed acyclic graphs (DAGs) on heterogeneous distributed systems is a classical NP-hard problem, and the list-scheduling heuristic HEFT remains the defacto standard because of its low complexity and strong makespan. In real deployments, however, machines fail: commodity and pre-emptible nodes are far less reliable than dedicated ones, and a makespan-optimal but reliability-agnostic placement can be dramatically slowed by node failures. We show empirically, on real workflow structures from the WfCommons/Pegasus corpus, that no single fixed heuristic is best across the joint space of failure intensity and cluster load: with no failures HEFT is optimal, whereas under failures a reliability-aware placement can reduce the expected makespan by up to 52% when spare capacity exists. Motivated by this, we present TFR-GNN, a graph neural scheduler that combines bidirectional dependency attention over the task DAG, topology attention over the (bandwidth-weighted) machine graph, and a cross-attention placement head augmented with a failure-gated reliability tilt and an optional replication gate. We train TFR-GNN by distilling a best-of-portfolio fault-tolerant oracle into a single one-shot policy. On real workflows and a bimodal-reliability cluster model, TFR-GNN matches HEFT exactly when there are no failures, reduces the expected makespan under failures by 14.8% on average (up to 47%) over HEFT, beats a fixed reliability-aware baseline (R-HEFT) by 11% and matches a per-scenario hindsight oracle as a single policy without any deployment-time tuning, and generalises to unseen applications and to workflows an order of magnitude larger than those seen in training, while producing schedules in well under a second for graphs of nearly 5,000 tasks. All results are produced by a verified event-level simulator on real workflow data; no experimental numbers are synthetic.