论文标题
通过动态任务放置对Edge-Cloud无服务器平台的性能优化
Performance Optimization for Edge-Cloud Serverless Platforms via Dynamic Task Placement
论文作者
论文摘要
我们提出了一个使用动态任务放置在无服务器边缘云平台中性能优化的框架。我们专注于智能边缘设备的应用程序，例如智能摄像机或扬声器，这些应用程序需要在实际到近乎实现的时间内执行输入数据上的处理任务。我们的框架允许用户指定每个应用程序任务的成本和延迟要求，并且对于每个输入，它确定是在边缘设备还是在云中执行任务。此外，对于云执行，该框架标识满足性能目标所需的容器资源配置。我们使用从AWS Lambda和AWS Greengregrass中收集的无服务器应用程序收集的测量结果评估了模拟框架。此外，我们已经实施了在这些相同平台中运行的框架的原型。在使用原型的实验中，我们的模型可以预测平均端到端潜伏期，而误差少于6％，并且与仅边缘执行相比，端到端潜伏期的近三个数量级降低。
We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing tasks on input data in real to near-real time. Our framework allows the user to specify cost and latency requirements for each application task, and for each input, it determines whether to execute the task on the edge device or in the cloud. Further, for cloud executions, the framework identifies the container resource configuration needed to satisfy the performance goals. We have evaluated our framework in simulation using measurements collected from serverless applications in AWS Lambda and AWS Greengrass. In addition, we have implemented a prototype of our framework that runs in these same platforms. In experiments with our prototype, our models can predict average end-to-end latency with less than 6% error, and we obtain almost three orders of magnitude reduction in end-to-end latency compared to edge-only execution.
