Data of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage

This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper submitted to QRS22 with the title: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage Abstract: Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to a container when it is idle, current scheduling technologies have limitations. In this paper, we propose our approach to efficiently manage cloud resource usage when containers are idle. For this purpose, we deplete idle containers in a stateful manner, i.e., we propose a novel manager that monitors idle containers, saves their state and efficiently depletes them. When a reconstruction is needed, this manager reconstructs a depleted container using the saved state. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling, and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 7.71% averaged over public and private clouds.

本在线附属材料包含为提交至QRS22的研究论文所开展实验的代码、数据集与评估日志，该论文标题为：面向高效资源利用的云节点容器有状态释放与调度。

摘要：容器调度是当前服务与基于云的应用的核心组成部分。调度器的运行层级取决于系统开发者所拥有的管控权限。一方面，容器编排管理器（Container Orchestration Manager）例如谷歌Kubernetes，负责将容器调度至不同节点。另一方面，无服务器管理器（Serverless Manager）例如谷歌Autopilot，可自动管控底层基础设施，开发者无需手动管理节点。然而，在容器释放（即当容器处于空闲状态时，移除其已分配的云资源）这一场景下，当前调度技术仍存在局限性。本文提出一种可在容器空闲时高效管控云资源使用的方案。为此，我们采用有状态方式释放空闲容器：即开发一款新型管理器，用于监控空闲容器、保存其运行状态，并对其进行高效释放。当需要重建容器时，该管理器可利用已保存的状态重建已释放的容器。在本方案中，我们提出一项基础设施即代码（Infrastructure as Code）组件，用于在无法将已释放容器调度至原节点（例如因原节点负载过高）时，自动创建新节点。我们针对容器的有状态释放与重调度构建了分析模型，并通过实证评估了该模型的准确性。为此，我们分别在私有云基础设施与谷歌云平台（Google Cloud Platform）上开展了实验。实验结果表明，我们的模型在公有云与私有云场景下的平均错误率仅为7.71%。