As an emerging technology in cloud computing Docker is becoming increasingly popular due to its high speed high efficiency and portability. The integration of Docker with OpenStack has been a hot topic in research and industrial areas e.g. as an emulation platform for evaluating cyberspace security technologies. This paper introduces a high-performance Docker integration scheme based on OpenStack that implements a container management service called Yun. Yun interacts with OpenStack’s services and manages the lifecycle of the container through the Docker Engine to integrate OpenStack and Docker. Yun improves the container deployment and throughput as well as the system performance by optimizing the message transmission architecture between internal components the underlying network data transmission architecture between containers and the scheduling methods. Based on the Docker Engine API Yun provides users with interfaces for CPU memory and disk resource limits to satisfy precise resource limits. Regarding scheduling Yun introduces a new NUMA-aware and resource-utilization-aware scheduling model to improve the performance of containers under resource competition and to balance the load of computing resources. Simultaneously Yun decouples from OpenStack versions by isolating its own running environment from the running environment of OpenStack to achieve better compatibility. Experiments show that compared to traditional methods Yun not only achieves the integration of OpenStack and Docker but also exhibits high performance in terms of deployment efficiency container throughput and the container’s system while also achieving load balancing.

中文翻译：

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基于OpenStack的高性能docker集成方案

Docker作为云计算中的新兴技术，由于其高速，高效和可移植性而变得越来越受欢迎。Docker与OpenStack的集成一直是研究和工业领域的热门话题，例如，作为评估网络空间安全技术的仿真平台。本文介绍了一种基于OpenStack的高性能Docker集成方案，该方案实现了名为Yun的容器管理服务。Yun与OpenStack的服务进行交互，并通过Docker Engine管理容器的生命周期，以集成OpenStack和Docker。Yun通过优化内部组件之间的消息传输体系结构，容器之间的底层网络数据传输体系结构和调度方法，提高了容器部署和吞吐量以及系统性能。Yun基于Docker Engine API，Yun为用户提供了CPU内存和磁盘资源限制的接口，以满足精确的资源限制。关于调度，Yun引入了一种新的NUMA感知和资源利用感知的调度模型，以提高在资源竞争下容器的性能并平衡计算资源的负载。同时，Yun通过将其自身的运行环境与OpenStack的运行环境隔离开来，从OpenStack版本中分离出来，以实现更好的兼容性。