  Edge computation offloading allows mobile end devices to put execution of
compute-intensive task on the edge servers. End devices can decide whether
offload the tasks to edge servers, cloud servers or execute locally according
to current network condition and devices' profile in an online manner. In this
article, we propose an edge computation offloading framework based on Deep
Imitation Learning (DIL) and Knowledge Distillation (KD), which assists end
devices to quickly make fine-grained decisions to optimize the delay of
computation tasks online. We formalize computation offloading problem into a
multi-label classification problem. Training samples for our DIL model are
generated in an offline manner. After model is trained, we leverage knowledge
distillation to obtain a lightweight DIL model, by which we further reduce the
model's inference delay. Numerical experiment shows that the offloading
decisions made by our model outperforms those made by other related policies in
latency metric. Also, our model has the shortest inference delay among all
policies.
