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Optimized container scheduling for data-intensive serverless edge computing

机译：用于数据密集无服务器边缘计算的优化容器调度

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摘要

Operating data-intensive applications on edge systems is challenging, due to the extreme workload and device heterogeneity, as well as the geographic dispersion of compute and storage infrastructure. Serverless computing has emerged as a compelling model to manage the complexity of such systems, by decoupling the underlying infrastructure and scaling mechanisms from applications. Although serverless platforms have reached a high level of maturity, we have found several limiting factors that inhibit their use in an edge setting. This paper presents a container scheduling system that enables such platforms to make efficient use of edge infrastructures. Our scheduler makes heuristic trade-offs between data and computation movement, and considers workload-specific compute requirements such as GPU acceleration. Furthermore, we present a method to automatically fine-tune the weights of scheduling constraints to optimize high-level operational objectives such as minimizing task execution time, uplink usage, or cloud execution cost. We implement a prototype that targets the container orchestration system Kubemetes, and deploy it on an edge testbed we have built. We evaluate our system with trace-driven simulations in different infrastructure scenarios, using traces generated from running representative workloads on our testbed. Our results show that (a) our scheduler significantly improves the quality of task placement compared to the state-of-the-art scheduler of Kubemetes, and (b) our method for fine-tuning scheduling parameters helps significantly in meeting operational goals.
机译：由于极端的工作量和设备异质性，以及计算和存储基础设施的地理分散，在边缘系统上运行数据密集型应用是具有挑战性的，以及计算和存储基础设施的地理分散。无服务器计算出现为令人信服的模型来管理这些系统的复杂性，通过解耦底层基础设施和从应用程序的缩放机制。虽然无服务器平台已达到高度的成熟度，但我们发现了几个限制因素，抑制了它们在边缘设置中的使用。本文介绍了一个集装箱调度系统，使等平台能够有效地利用边缘基础架构。我们的调度程序在数据和计算移动之间进行启发式权衡，并考虑特定于工作负载的计算要求，例如GPU加速。此外，我们提出了一种自动微调调度约束的权重的方法，以优化高级操作目标，例如最小化任务执行时间，上行使用或云执行成本。我们实现了一个针对容器编排系统Kubemetes的原型，并在我们构建的边缘测试中部署它。我们在不同的基础架构方案中评估了我们的系统，在不同的基础架构方案中使用了在我们的测试平台上运行代表性工作负载生成的痕迹。我们的结果表明，与kubemetes的最先进的调度程序相比，我们的调度程序显着提高了任务安排的质量，以及我们进行的微调调度参数的方法，有助于满足运营目标。

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