The fifth generation (5G) of wireless networks is set out to meet the
stringent requirements of vehicular use cases. Edge computing resources can aid
in this direction by moving processing closer to end-users, reducing latency.
However, given the stochastic nature of traffic loads and availability of
physical resources, appropriate auto-scaling mechanisms need to be employed to
support cost-efficient and performant services. To this end, we employ Deep
Reinforcement Learning (DRL) for vertical scaling in Edge computing to support
vehicular-to-network communications. We address the problem using Deep
Deterministic Policy Gradient (DDPG). As DDPG is a model-free off-policy
algorithm for learning continuous actions, we introduce a discretization
approach to support discrete scaling actions. Thus we address scalability
problems inherent to high-dimensional discrete action spaces. Employing a
real-world vehicular trace data set, we show that DDPG outperforms existing
solutions, reducing (at minimum) the average number of active CPUs by 23% while
increasing the long-term reward by 24%.

本文使用深度强化学习的方法解决边缘计算中垂直缩放的问题，为车联网通信提供支持，实验表明，与现有解决方案相比，该方法可以降低至少 23％ 的 CPU 使用率，同时增加 24％ 的长期收益。