Cypress: input size-sensitive container provisioning and request scheduling for serverless platforms
Cypress: input size-sensitive container provisioning and request scheduling for serverless platforms
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DOI：
10.1145/3542929.3563464
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发表时间：
2022-11
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通讯作者：
Vivek M. Bhasi;Jashwant Raj Gunasekaran;Aakash Sharma;M. Kandemir;C. Das
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作者：
Vivek M. Bhasi;Jashwant Raj Gunasekaran;Aakash Sharma;M. Kandemir;C. Das
无服务器平台的日益普及使在其上部署的应用程序数量和多样性增加。这些应用程序中的大多数处理用户提供的输入以产生所需的结果。在经验上，在输入敏感分析领域的现有工作表明，许多这样的应用程序具有输入尺寸依赖性执行时间，可以通过建模技术确定。但是，现有的无服务器资源管理框架对这些应用程序的输入尺寸敏感性不可知。我们在本文中证明，这可能会导致容器过度提供和/或端到端服务水平目标（SLO）违规行为。为了解决这个问题，我们提出了一个对输入尺寸敏感资源管理框架的赛普拉斯（Cypress），以最大程度地减少为应用程序提供的容器，同时确保高度的SLO合规性。我们使用来自AWS无服务器应用程序存储库中的5个应用程序和/或Open-FAAS功能商店对Kubernetes管理集群的柏树进行了广泛的评估，并具有真实的输入大小分布。我们的实验结果表明，柏树产生的容器最多减少了66％，从而将容器的利用率和整个集群范围的能量分别提高了2.95倍和23％，而不是先进的框架，同时仍然具有高度的SLO固定框架（高达99.99％）。
The growing popularity of the serverless platform has seen an increase in the number and variety of applications (apps) being deployed on it. The majority of these apps process user-provided input to produce the desired results. Existing work in the area of input-sensitive profiling has empirically shown that many such apps have input size-dependent execution times which can be determined through modelling techniques. Nevertheless, existing serverless resource management frameworks are agnostic to the input size-sensitive nature of these apps. We demonstrate in this paper that this can potentially lead to container over-provisioning and/or end-to-end Service Level Objective (SLO) violations. To address this, we propose Cypress, an input size-sensitive resource management framework, that minimizes the containers provisioned for apps, while ensuring a high degree of SLO compliance. We perform an extensive evaluation of Cypress on top of a Kubernetes-managed cluster using 5 apps from the AWS Serverless Application Repository and/or Open-FaaS Function Store with real-world traces and varied input size distributions. Our experimental results show that Cypress spawns up to 66% fewer containers, thereby, improving container utilization and saving cluster-wide energy by up to 2.95X and 23%, respectively, versus state-of-the-art frameworks, while remaining highly SLO-compliant (up to 99.99%).