  Multi-edge cooperative computing that combines constrained resources of
multiple edges into a powerful resource pool has the potential to deliver great
benefits, such as a tremendous computing power, improved response time, more
diversified services. However, the mass heterogeneous resources composition and
lack of scheduling strategies make the modeling and cooperating of multi-edge
computing system particularly complicated. This paper first proposes a
system-level state evaluation model to shield the complex hardware
configurations and redefine the different service capabilities at heterogeneous
edges. Secondly, an integer linear programming model is designed to cater for
optimally dispatching the distributed arriving requests. Finally, a
learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS
embeds the real-time states of multi-edge system and requests information, and
combines the embeddings with a policy network to schedule the requests, so that
the response time of all requests can be minimized. Evaluation results verify
that CoRaiS can make a high-quality scheduling decision in real time, and can
be generalized to other multi-edge computing system, regardless of system
scales. Characteristic validation also demonstrates that CoRaiS successfully
learns to balance loads, perceive real-time state and recognize heterogeneity
while scheduling.
