Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum

Workflow offloading in the edge-to-cloud continuum copes with an extended calculation network among edge devices and cloud platforms. With the growing significance of edge and cloud technologies, workflow offloading among these environments has been investigated in recent years. However, the dynamics of offloading optimization objectives, i.e., latency, resource utilization rate, and energy consumption among the edge and cloud sides, have hardly been researched. Consequently, the Quality of Service(QoS) and offloading performance also experience uncertain deviation. In this work, we propose a multi-objective robust offloading algorithm to address this issue, dealing with dynamics and multi-objective optimization. The workflow request model in this work is modeled as Directed Acyclic Graph(DAG). An LSTM-based sequence-to-sequence neural network learns the offloading policy. We then conduct comprehensive implementations to validate the robustness of our algorithm. As a result, our algorithm achieves better offloading performance regarding each objective and faster adaptation to newly changed environments than fine-tuned typical single-objective RL-based offloading methods.