  With the mass deployment of computing-intensive applications and
delay-sensitive applications on end devices, only adequate computing resources
can meet differentiated services' delay requirements. By offloading tasks to
cloud servers or edge servers, computation offloading can alleviate computing
and storage limitations and reduce delay and energy consumption. However, few
of the existing offloading schemes take into consideration the cloud-edge
collaboration and the constraint of energy consumption and task dependency.
This paper builds a collaborative computation offloading model in cloud and
edge computing and formulates a multi-objective optimization problem.
Constructed by fusing optimal transport and Policy-Based RL, we propose an
Optimal-Transport-Based RL approach to resolve the offloading problem and make
the optimal offloading decision for minimizing the overall cost of delay and
energy consumption. Simulation results show that the proposed approach can
effectively reduce the cost and significantly outperforms existing optimization
solutions.
