Mobile Edge Computing (MEC) refers to the concept of placing computational capability and applications at the edge of the network, providing benefits such as reduced latency in handling client requests, reduced network congestion, and improved performance of applications. The performance and reliability of MEC are degraded significantly when one or several edge servers in the cluster are overloaded. Especially when a server crashes due to the overload, it causes service failures in MEC. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings - computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.
翻译:移动边缘计算(MEC) 指的是将计算能力和应用程序置于网络边缘的概念,提供一些好处,例如处理客户请求的延迟性降低,降低网络拥塞,提高应用程序的性能。当组群中的一个或几个边缘服务器超载时,MEC的性能和可靠性会大大降低。特别是当服务器因超载而崩溃时,它会给MEC造成服务故障。在这项工作中,提出了防止边缘节点超负荷的适应性接收控制政策。这个方法基于最近提出的一个叫做SALMUT(重力学习)的简单易懂的低复杂RL(重力学习)算法,称为SALMUT(多临界值的系统结构-软件学习),该算法利用多级列队列的最佳入场控制政策结构,以平均成本设定。我们将框架扩大到在折扣成本环境下避免超负荷保护问题。在两种不同环境下模拟真实世界部署的情景中,计算机模拟和模拟测试床。我们的经验评估显示,在深度定位C(Prorial-A-LUTA) 和深级政策中的总折扣成本是像SAL-PUTA-LA一样。