A Dynamic Application Configuration and Capacity-Aware Offloading System for MEC Optimization

Mobile Edge Computing (MEC) is a key technology for enabling energy-efficient and low-latency processing of computation-intensive applications through task offloading. However, current frameworks typically model dependent tasks using static Directed Acyclic Graphs (DAGs), which are poorly suited to dynamic edge environments characterized by fluctuating resources and diverse QoS demands. These fixed DAG structures often fail to adapt to runtime changes in bandwidth or node workload, leading to frequent task failures or inefficient resource usage. To overcome these limitations, we propose Dynamic Application Configuration, a paradigm that allows applications to switch between multiple predefined DAG variants at runtime. This enables the system to dynamically balance accuracy and resource efficiency at critical decision points by adapting to current network and computing conditions. Based on this concept, we design the Dynamic Application Configuration and Capacityaware task offloading System (DACCS), which employs a two-step offloading strategy: (i) a Dynamic Graph Selection (DGS) algorithm that adaptively adjusts application configurations during key subtask execution based on real-time resource states, and (ii) a dependency-aware offloading algorithm (DAP) that optimizes offloading assignments by jointly optimizing the application completion rate, processing delay and effective utilization rate. Experiments and real-system validations demonstrate that DGS improves the service performance across multiple strategies. Furthermore, the proposed DGS-DAP strategy outperforms other benchmark approaches under dynamic conditions.