🤖 AI Summary
This work addresses the limitations of existing edge service orchestration approaches, which overlook the reorderability of local sequences within service function chain directed acyclic graphs (SFC-DAGs) and fail to jointly optimize request scheduling and resource allocation. To bridge this gap, the authors propose RQ-SAFE, a novel framework that, for the first time, couples the flexibility of SFC-DAG local ordering with queue-aware resource scheduling. RQ-SAFE dynamically evaluates the resource impact of feasible local orderings in an online manner and leverages real-time queue states to guide virtual network function (VNF) instance selection and path construction. Furthermore, it incorporates a learning-assisted reordering mechanism to balance quality of service (QoS) and system load. Experimental results demonstrate that, compared to the GNN-DAG-Score baseline, RQ-SAFE reduces CPU load imbalance by 6.1%, lowers peak utilization by 2.3%, and improves QoS by 4.53 percentage points through joint optimization.
📝 Abstract
Intent-driven edge services allow multiple virtual network function (VNF) segments in a service function chain directed acyclic graph (SFC-DAG) to be locally reordered without changing service semantics, creating richer request-side orchestration freedom. Existing orchestration methods mainly optimize VNF placement, routing, or queue-aware scheduling for a predetermined service order; they do not fully exploit this freedom or couple it with runtime resource scheduling. This paper presents RQ-SAFE, a request-resource coupled scheduling framework for online edge SFC-DAG orchestration with checked commitment. RQ-SAFE evaluates each feasible local order by previewing its resource-side consequences on the current edge infrastructure, and uses the retained order to guide VNF instance selection and path construction. Queue state is used throughout the decision process to evaluate local orders, rank per-VNF candidates, and perform final queue-aware quality-of-service (QoS) validation. A profile-aware learning-assisted re-ranker balances request-side QoS objectives and resource-side load objectives by refining retained top-K candidates. On matched edge SFC-DAG workloads, RQ-SAFE achieves comparable QoS-compliant service outcomes to graph-aware baselines while improving resource balance. Relative to the graph neural network (GNN)-based GNN-DAG-Score baseline on public-mixed workloads, it reduces central processing unit (CPU) imbalance by 6.1% and peak CPU by 2.3%, with limited additional control-plane decision time. Ablation results show that enabling local-order flexibility and queue awareness together improves QoS by 4.53 percentage points over disabling both factors, with a 3.83 percentage-point positive interaction between the two factors. Overall, RQ-SAFE offers a practical request-resource coupling paradigm for exploiting orchestration freedom in intent-aware edge SFC-DAG services.