Bursty Workload-Adaptive Scheduling with Data Protections for Industrial Cloud–Edge Collaboration

Abstract Industrial Internet applications impose stringent latency and security requirements on cloud–edge collaborative computing. Existing task-scheduling schemes, however, suffer from two critical shortcomings: (i) sensitive arrtibute data of computing nodes may be leaked or tampered with during transmission or scheduling; (ii) they lack efficient resource-task matching when bursty workloads coincide with limited node capacities. To overcome these challenges, we present BWAS-DP—Bursty Workload-Adaptive Scheduling with Data Protections mechanism for Industrial Cloud–Edge Collaboration. First, we introduce a confidentiality–integrity–authenticity method that employs an enhanced zero-knowledge range proof for real-valued ranges together with digital signatures, guaranteeing the security of node attribute data against any adversary except the node itself. Second, we propose a bursty workload-adaptive scheduling method that combines a congestion-aware dynamic resource-reservation based on Bayesian-optimized LSTM networks with a multi-objective task-scheduling optimization employing task/node-adaptive strategies to enhance decision efficiency. We have implemented a prototype system; extensive experiments on both customized and public industrial datasets show that BWAS-DP reduces average latency by 19.2% under anticipated workloads and by 24.8% under bursty workloads, improves the security metric by approximately 11.4%, and increases decision-making efficiency by roughly 7.2%, thereby delivering lower latency, greater stability, and stronger security.