COATWD: Cost-Optimized Adaptive Three-Way Decision for Cloud–Edge Task Orchestration

With the increasing number of end devices and the growing heterogeneity of application demands, task orchestration in dynamic cloud–edge environments faces challenges arising from network fluctuations, resource contention, and decision uncertainty. To address these challenges, this paper proposes a Cost-Optimized Adaptive Three-Way Decision (COATWD) method for cloud–edge task orchestration. Candidate execution costs for Local Edge, Remote Edge, and Cloud are evaluated by jointly considering network transmission, task queuing, task execution, and QoS constraints, while historical prediction residuals provide empirical evidence for estimating candidate-superiority probabilities. Based on the current task characteristics and candidate resource states, decision losses are dynamically constructed, and adaptive dual thresholds are generated according to the effectiveness of historical refinement, thereby partitioning candidate relations into positive, boundary, and negative regions. Candidate relations assigned to the boundary region are further refined by matching historical information to the task type, candidate execution role, and specific Edge host. Observed execution outcomes continuously update prediction residuals and task success and failure statistics, thereby forming a closed-loop, online, adaptive orchestration process. Experimental results obtained with EdgeCloudSim 4.0 show that COATWD effectively reduces the task failure rate, average service time, and average processing time under different device scales and application workloads.