论文标题
使用Aquatope的多相无服务器工作流程的QoS感知资源管理
QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope
论文作者
论文摘要
多阶段的无服务器应用程序，即具有许多计算和I/O阶段的工作流，正越来越多地代表FAAS平台。尽管在细粒度的可伸缩性和模块化开发方面具有优势，但这些应用程序的性能，资源降低效率低下和高成本的高度比以前的简单无服务器功能更大。 我们提出了Aquatope，这是端到端无服务器工作流的QoS and and-and-and-and-and Take-Inave Resource调度程序，它考虑了FAAS平台中存在的固有不确定性，并提高了性能可预测性和资源效率。 Aquatope使用一组可扩展和经过验证的贝叶斯模型在功能调用之前创建预热的容器，并在功能粒度上分配适当的资源，以满足复杂的工作流程的端到端QoS，同时最小化资源成本。在各种分析和交互式多阶段无服务器工作负载中，Aquatope明显胜过先前的系统，将QoS违规的措施降低5倍，而与其他QoS-Cos-Cosing方法相比，平均成本降低了34％，高达52％。
Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these applications are subject to suboptimal performance, resource inefficiency, and high costs to a larger degree than previous simple serverless functions. We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for end-to-end serverless workflows that takes into account the inherent uncertainty present in FaaS platforms, and improves performance predictability and resource efficiency. Aquatope uses a set of scalable and validated Bayesian models to create pre-warmed containers ahead of function invocations, and to allocate appropriate resources at function granularity to meet a complex workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set of analytics and interactive multi-stage serverless workloads, Aquatope significantly outperforms prior systems, reducing QoS violations by 5x, and cost by 34% on average and up to 52% compared to other QoS-meeting methods.
