Kraken: Adaptive Container Provisioning for Deploying Dynamic DAGs in Serverless Platforms
Kraken: Adaptive Container Provisioning for Deploying Dynamic DAGs in Serverless Platforms
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DOI：
10.1145/3472883.3486992
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发表时间：
2021-11
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影响因子：
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通讯作者：
Vivek M. Bhasi;Jashwant Raj Gunasekaran;P. Thinakaran;Cyan Subhra Mishra;M. Kandemir;C. Das
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作者：
Vivek M. Bhasi;Jashwant Raj Gunasekaran;P. Thinakaran;Cyan Subhra Mishra;M. Kandemir;C. Das
微服务的日益普及导致了基于在线云服务的应用程序的扩散，这些应用程序通常按照定向的无环图（DAG）建模，包括数十至数百个微服务。这些应用程序中的绝大多数是面向用户的，因此具有严格的SLO要求。无服务器功能，具有简短的资源配置时间和即时可扩展性，是用于开发此类关键延迟应用程序的合适候选者。但是，现有的无服务器提供商不知道应用程序DAG的工作流程特性，从而导致容器过度提供。在动态dags的情况下，这进一步加剧了这一点，在动态dags的情况下，该应用程序的函数链不知道先验。在这些观察结果的激励下，我们提出了Kraken，Kraken是一个工作流吸引的资源管理框架，可最大程度地减少为应用程序DAG提供的容器数量，同时确保SLO符合性。我们在OpenFAAS上设计和实施Kraken，并在多节点Kubernetes管理的集群上进行评估。我们使用DeathStarbench Workload Suite和Real-World Traces进行了广泛的实验评估，表明，与在无用平台中使用的最先进的现有调度程序相比，Kraken的卵产生的容器最多减少了76％，从而提高了容器利用率和节省群集的能源的76％。
The growing popularity of microservices has led to the proliferation of online cloud service-based applications, which are typically modelled as Directed Acyclic Graphs (DAGs) comprising of tens to hundreds of microservices. The vast majority of these applications are user-facing, and hence, have stringent SLO requirements. Serverless functions, having short resource provisioning times and instant scalability, are suitable candidates for developing such latency-critical applications. However, existing serverless providers are unaware of the workflow characteristics of application DAGs, leading to container over-provisioning in many cases. This is further exacerbated in the case of dynamic DAGs, where the function chain for an application is not known a priori. Motivated by these observations, we propose Kraken, a workflow-aware resource management framework that minimizes the number of containers provisioned for an application DAG while ensuring SLO-compliance. We design and implement Kraken on OpenFaaS and evaluate it on a multi-node Kubernetes-managed cluster. Our extensive experimental evaluation using DeathStarbench workload suite and real-world traces demonstrates that Kraken spawns up to 76% fewer containers, thereby improving container utilization and saving cluster-wide energy by up to 4x and 48%, respectively, when compared to state-of-the art schedulers employed in serverless platforms.