[论文解读] Containers Orchestration with Cost-Efficient Autoscaling in Cloud Computing Environments

Maria A. Rodriguez, Rajkumar Buyya|arXiv (Cornell University)|Dec 2, 2018

Cloud Computing and Resource Management被引用 10

一句话总结

本文提出了一种面向云环境的成本高效容器编排框架，通过为Kubernetes设计的插件调度器实现动态集群资源扩展和容器放置优化。结合工作负载感知的自动伸缩与运行时重调度机制，该方法在真实云基础设施上相比Kubernetes默认调度器将基础设施成本降低了58%。

ABSTRACT

Containers are standalone, self-contained units that package software and its dependencies together. They offer lightweight performance isolation, fast and flexible deployment, and fine-grained resource sharing. They have gained popularity in better application management and deployment in recent years and are being widely used by organizations to deploy their increasingly diverse workloads such as web services, big data, and IoT in either proprietary clusters or cloud data centres. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms are tailored to optimize the scheduling of containers on a fixed-sized private cluster but are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. In this work, we propose a comprehensive container resource management approach that has three different objectives. The first one is to optimize the initial placement of containers by efficiently scheduling them on existing resources. The second one is to autoscale the number of resources at runtime based on the current cluster's workload. The third one is a rescheduling mechanism to further support the efficient use of resources by consolidating applications into fewer VMs when possible. Our algorithms are implemented as a plugin-scheduler for Kubernetes platform. We evaluated our framework and the effectiveness of the proposed algorithms on an Australian national cloud infrastructure. Our experiments demonstrate that considerable cost savings can be achieved by dynamically managing the cluster size and placement of applications. We find that our proposed approaches are capable of reducing the cost by 58% when compared to the default Kubernetes scheduler.

研究动机与目标

• 解决现有容器编排平台缺乏成本感知自动伸缩和云原生优化的差距。
• 通过智能调度容器至云集群中可用的虚拟机，提升资源利用率。
• 基于实时工作负载需求，实现集群规模的动态扩展。
• 通过运行时重调度将应用集中到更少的虚拟机上，降低运营成本。
• 实现并评估一个集成成本感知调度与自动伸缩功能的Kubernetes插件调度器。

提出的方法

• 设计一个扩展默认调度器的Kubernetes插件调度器，引入成本感知的容器放置逻辑。
• 实现一个工作负载监控模块，用于收集CPU、内存和网络使用情况的实时指标，以支持动态伸缩决策。
• 开发一种自动伸缩算法，根据历史和当前的工作负载趋势动态增减虚拟机数量。
• 引入一种重调度机制，识别资源利用率低的虚拟机，并将容器迁移以实现工作负载整合。
• 采用考虑可变云定价和虚拟机配置成本的计价模型，指导调度与伸缩决策。
• 在澳大利亚国家级云基础设施上，基于真实工作负载对框架进行评估。

实验结果

研究问题

• RQ1 如何增强容器编排以支持公共云环境中的动态自动伸缩？
• RQ2 将智能容器放置与集群自动伸缩相结合，能在多大程度上实现成本节约？
• RQ3 运行时重调度在整合工作负载并减少虚拟机规模方面有多高效？
• RQ4 与Kubernetes默认调度器相比，所提出的调度器在性能和成本方面有何影响？
• RQ5 在大规模云部署中，该框架能否在最小化基础设施成本的同时维持服务质量？

主要发现

• 在真实云基础设施上，所提出的框架相比Kubernetes默认调度器实现了58%的基础设施成本降低。
• 自动伸缩机制能有效响应工作负载波动，避免过度配置资源。
• 重调度组件成功将应用集中到更少的虚拟机上，提升了资源利用率并减少了空闲容量。
• 插件调度器在多样化工作负载下保持了低调度延迟和高放置效率。
• 该框架在生产级云环境中展现出强大的可扩展性和适应能力。
• 在可变且突发性的工作负载下，成本节约最为显著，凸显了动态资源管理的价值。