Budget-Constrained Edge Orchestration of DAG-Based Sensor-Data Workflows with Multicast Result Dissemination

Distributed sensing applications increasingly transform observations through multi-stage pipelines executed across heterogeneous edge-cloud resources. A sensing request is operationally complete only after its actionable output reaches all subscribed monitoring or control endpoints. However, workflow scheduling and result dissemination are commonly optimized separately, even though the server executing the sink task determines the source of dissemination. This paper studies their coupling under a per-request budget. We model each sensor-data workflow as a directed acyclic graph of service-dependent processing stages and intermediate-data transfers, followed by shared delivery of the sink output. We formulate lexicographic minimization of end-to-end sensing-response delay and total cost, subject to precedence, server capacity, service availability, multicast connectivity, and budget constraints. We then propose Priority-Aware Constraint-based Large Neighborhood Search (PAC-LNS), which combines topology-aware prioritization, dynamic ready-window scheduling, bounded local refinement, dissemination-budget reservation, and shared-tree construction. We implement PAC-LNS scheduling and shared-tree construction in MATLAB and evaluate synthetic DAGs and Alibaba trace-derived computation workloads: feasibility determines admission, delay and cost are compared on common successful requests, and acceptance rate and online runtime are reported separately. In trace replay with R=5000, PAC-LNS accepts 89.04-90.12% of requests as the budget increases from B=100 to B=200, while its online scheduling and tree-construction time averages 16.644 ms per request across these settings. These results support joint processing and delivery orchestration for budget-constrained edge-assisted sensing services.