With rapid advances in containerization techniques, the serverless computing model is becoming a valid candidate execution model in edge networking, similar to the widely used cloud model for applications that are stateless, single purpose and event-driven, and in particular for delay-sensitive applications. One of the cloud serverless processes, i.e., the auto-scaling mechanism, cannot be however directly applied at the edge, due to the distributed nature of edge nodes, the difficulty of optimal resource allocation, and the delay sensitivity of workloads. We propose a solution to the auto-scaling problem by applying reinforcement learning (RL) approach to solving problem of efficient scaling and resource allocation of serverless functions in edge networks. We compare RL and Deep RL algorithms with empirical, monitoring-based heuristics, considering delay-sensitive applications. The simulation results shows that RL algorithm outperforms the standard, monitoring-based algorithms in terms of total delay of function requests, while achieving an improvement in delay performance by up to 50%.

翻译：随着容器化技术的快速发展，无服务器计算模型正成为边缘网络中一种有效的候选执行模型，类似于广泛使用的云计算模型，适用于无状态、单一目的且事件驱动的应用，尤其是对延迟敏感的应用。然而，由于边缘节点的分布式特性、资源优化分配的难度以及工作负载对延迟的敏感性，云无服务器流程中的弹性伸缩机制无法直接应用于边缘环境。我们提出了一种通过强化学习（RL）方法解决弹性伸缩问题的方案，以实现边缘网络中无服务器函数的高效伸缩和资源分配。我们基于对延迟敏感的应用场景，将RL和深度强化学习（Deep RL）算法与基于监控的启发式经验方法进行了比较。仿真结果表明，在函数请求的总延迟方面，RL算法优于标准的基于监控的算法，延迟性能提升高达50%。