Multi-access edge computing (MEC) and network function virtualization (NFV)
are promising technologies to support emerging IoT applications, especially
those computation-intensive. In NFV-enabled MEC environment, service function
chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be
mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive
applications, which can be represented by SFCs, fully or partially to MEC
servers for remote execution. This paper studies the partial offloading and SFC
mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where
an incoming task can be partitioned into two parts, one for local execution and
the other for remote execution. The objective is to minimize the average cost
in the long term which is a combination of execution delay, MD's energy
consumption, and usage charge for edge computing. This problem consists of two
closely related decision-making steps, namely task partition and VNF placement,
which is highly complex and quite challenging. To address this, we propose a
cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where we
design a framework enabling interaction between two agents. Simulation results
show that the proposed algorithm outperforms three combinations of deep
reinforcement learning algorithms in terms of cumulative and average episodic
rewards and it overweighs a number of baseline algorithms with respect to
execution delay, energy consumption, and usage charge.

本研究使用深度强化学习算法解决在 NFV-enabled MEC 环境下的部分卸载和服务功能链映射问题，旨在在执行延迟，移动设备的能量消耗和边缘计算的使用费用方面优化。研究结果表明，所提出的算法在累积和平均随机回报方面优于三种深度强化学习算法的组合，并在执行延迟，能量消耗和使用费用方面胜过众多基准算法。