V2N Service Scaling with Deep Reinforcement Learning
This addresses the challenge of efficient resource scaling for vehicular communications in 5G networks, but it is incremental as it applies an existing DRL method with modifications to a specific domain.
The paper tackled the problem of auto-scaling edge computing resources for cost-efficient and performant vehicular-to-network services in 5G networks, using Deep Reinforcement Learning with a discretization approach, and showed that it reduces the average number of active CPUs by at least 23% and increases the long-term reward by 24% compared to existing solutions.
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%.