论文标题
时间序列预测方法，以最大程度地减少云服务平台中的冷启动时间
A Time Series Forecasting Approach to Minimize Cold Start Time in Cloud-Serverless Platform
论文作者
论文摘要
无服务器计算是一个流行语，在技术领域以及开发人员和企业中通常使用。使用无服务器的功能-AS-Service（FAAS）模型，可以轻松地将其应用程序部署到云中，并在几天之内进行直播，它有助于开发人员专注于他们的核心业务逻辑和后端过程，例如管理基础架构，应用程序的应用程序，软件的更新和其他依赖关系，以及其他依赖关系由云服务提供者进行处理。无服务器计算的功能之一是能够将容器扩展到零，这导致了一个称为冷启动的问题。具有挑战性的部分是在不消耗额外资源的情况下减少冷启动潜伏期。在本文中，我们使用Sarima（季节性自动回归综合移动平均线），这是经典时间序列预测模型之一来预测传入请求的到来的时间，并因此增加或减少所需容器的数量以最大程度地减少资源浪费，从而减少功能启动时间。最后，我们实现了PBA（基于预测的自动化器），并将其与默认的HPA（水平POD Autoscaler）进行比较，该HPA与Kubernetes内置相比。结果表明，PBA的性能要比默认HPA好，同时减少了资源的浪费。
Serverless computing is a buzzword that is being used commonly in the world of technology and among developers and businesses. Using the Function-as-a-Service (FaaS) model of serverless, one can easily deploy their applications to the cloud and go live in a matter of days, it facilitates the developers to focus on their core business logic and the backend process such as managing the infrastructure, scaling of the application, updation of software and other dependencies is handled by the Cloud Service Provider. One of the features of serverless computing is ability to scale the containers to zero, which results in a problem called cold start. The challenging part is to reduce the cold start latency without the consumption of extra resources. In this paper, we use SARIMA (Seasonal Auto Regressive Integrated Moving Average), one of the classical time series forecasting models to predict the time at which the incoming request comes, and accordingly increase or decrease the amount of required containers to minimize the resource wastage, thus reducing the function launching time. Finally, we implement PBA (Prediction Based Autoscaler) and compare it with the default HPA (Horizontal Pod Autoscaler), which comes inbuilt with kubernetes. The results showed that PBA performs fairly better than the default HPA, while reducing the wastage of resources.
