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 singleobjective

RL-based offloading methods.