An Optimal-Transport-Based Reinforcement Learning Approach for Computation Offloading
For edge computing systems, this work provides a method to jointly optimize delay and energy in offloading decisions under task dependency constraints.
The paper addresses computation offloading in cloud-edge collaboration with constraints on energy and task dependency, proposing an optimal-transport-based reinforcement learning approach that minimizes delay and energy cost, outperforming existing solutions.
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.