论文标题
动态地满足服务网格上多个服务的性能目标
Dynamically meeting performance objectives for multiple services on a service mesh
论文作者
论文摘要
我们提出了一个框架，使服务提供商可以在不同的负载下实现端到端管理目标。动态控制动作由加固学习（RL）代理执行。我们的工作包括在实验室测试台上进行的实验和评估，我们在ISTIO和KUBERNETES平台支持的服务网格上实施了基本信息服务。我们研究了不同的管理目标，其中包括服务请求，吞吐量目标和服务差异的端到端延迟界限。这些目标被映射到RL代理人通过执行控制操作（即请求路由和请求阻止）来优化的奖励功能。我们在测试床上而是在模拟器中计算控制策略，该策略通过数量级加快了学习过程。在我们的方法中，系统模型是在测试床上学习的。然后，它用于实例化模拟器，该模拟器为各种管理目标制定了近乎最佳的控制策略。然后使用看不见的负载模式在测试床上评估学习的策略。
We present a framework that lets a service provider achieve end-to-end management objectives under varying load. Dynamic control actions are performed by a reinforcement learning (RL) agent. Our work includes experimentation and evaluation on a laboratory testbed where we have implemented basic information services on a service mesh supported by the Istio and Kubernetes platforms. We investigate different management objectives that include end-to-end delay bounds on service requests, throughput objectives, and service differentiation. These objectives are mapped onto reward functions that an RL agent learns to optimize, by executing control actions, namely, request routing and request blocking. We compute the control policies not on the testbed, but in a simulator, which speeds up the learning process by orders of magnitude. In our approach, the system model is learned on the testbed; it is then used to instantiate the simulator, which produces near-optimal control policies for various management objectives. The learned policies are then evaluated on the testbed using unseen load patterns.
