Configuration and Placement of Serverless Applications Using Statistical Learning
Configuration and Placement of Serverless Applications Using Statistical Learning
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DOI：
10.1109/tnsm.2023.3254437
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发表时间：
2023-06-01
影响因子：
5.3
通讯作者：
Huang, Lei
中科院分区：
文献类型：
--
作者：
Raza, Ali;Akhtar, Nabeel;Huang, Lei
在过去十年中，无服务器计算作为部署云应用程序和服务的一种新的引人注目的范例而出现。它用简化的编程模型代表了云计算的一种演变，其目的是抽象掉大多数操作问题。运行无服务器应用程序需要用户配置多个参数，如内存、CPU、云提供商等。虽然相对简单，但正确配置这些参数，同时最小化成本并满足延迟约束并非易事。在本文中，我们介绍了COSE，这是一个使用贝叶斯优化来查找无服务器应用程序中功能的最佳资源配置和放置的框架。COSE使用统计学习技术来智能地收集样本，并在不可见的配置值中预测无服务器功能的成本和执行时间。我们的框架使用可用位置上的预测成本和执行时间来选择“最佳”配置参数和位置，以便在满足客户目标的同时运行无服务器应用程序。我们用包含多个函数（线性链和服务图）的实际应用程序在AWS Lambda上评估了COSE，并成功地找到了最优/接近最优的配置。我们还在广泛的模拟分布式云环境中评估了COSE，以证实我们的方法的有效性。
In the last decade, serverless computing emerged as a new compelling paradigm for the deployment of cloud applications and services. It represents an evolution of cloud computing with a simplified programming model, that aims to abstract away most operational concerns. Running serverless applications requires users to configure multiple parameters, such as memory, CPU, cloud provider, etc. While relatively simpler, configuring such parameters correctly while minimizing cost and meeting delay constraints is not trivial. In this paper, we present COSE, a framework that uses Bayesian Optimization to find the optimal resource configuration and placement for functions in a serverless application. COSE uses statistical learning techniques to intelligently collect samples and predict the cost and execution time of a serverless function across unseen configuration values. Our framework uses the predicted cost and execution time on available locations to select the "best" configuration parameters and placement for running a serverless application while satisfying customer objectives. We evaluate COSE on AWS Lambda with real-world applications consisting of multiple functions (both linear chains and service graphs), where we successfully found optimal/near-optimal configurations. We also evaluate COSE over a wide range of simulated distributed cloud environments that confirm the efficacy of our approach.