UTOC: Uncertainty-aware Execution Optimization for Conditional DAG Application in MEC Networks

Mobile edge computing (MEC) is pivotal for delivering low-latency services to IoT applications. However, inherent uncertainties in real-world applications, notably conditional execution workflows and variable service durations, are often overlooked, hindering efficient resource utilization and optimization flexibility. This paper introduces UTOC, an uncertainty-aware framework that collaboratively optimizes the execution of conditional directed acyclic graph (CDAG) applications in MEC networks by co-designing service deployment and task scheduling. First, we model service duration variability with the G/G/c queueing model and represent conditional workflows with probabilistic branching. Then we employ moment matching theory to approximate application end-to-end (E2E) latency, enabling the accurate quantification of the impact on E2E latency by service instances. This quantification enables the CDAG application execution optimisation problem to be decomposed into two subproblems, service deployment and task scheduling. Finally, we design a heuristic service deployment algorithm for efficient resource allocation and an online scheduling algorithm to minimize E2E latency while balancing server congestion within the MEC networks. Extensive experiments validate that UTOC effectively utilizes edge resources and significantly reduces E2E latency compared to state-of-the-art baselines.