论文标题

C-Balancer:用于容器分析和调度的系统

C-Balancer: A System for Container Profiling and Scheduling

论文作者

Dhumal, Akshay, Janakiram, Dharanipragada

论文摘要

Linux容器近来已获得很高的知名度。这种受欢迎程度非常重要,这是由于容器比虚拟机(VM)的各种优势所致。这些容器轻巧,占据较小的存储空间,具有快速的启动时间,易于部署并具有更快的自动尺度。容器受欢迎的关键原因是,它们利用了微服务样式软件开发的机制,在该机构将应用程序设计为独立部署服务的情况下。有各种容器编排工具用于部署和管理集群中的容器。其中著名的是Docker Swarm和Kubernetes。但是,当将多个容器部署在节点上时,它们不会解决资源争议的影响。此外,在发生攻击或增加资源争夺时,它们不能为容器迁移提供支持。为了解决此类问题,我们提出了C-Balancer,这是一个调度框架,用于在集群环境中有效地放置容器。 C-Balancer通过定期分析容器并确定最佳容器到节点放置来起作用。我们提出的方法在资源利用和吞吐量方面提高了容器的性能。使用压力-NG和IPERF基准的工作负载组合的实验表明,我们提出的方法可实现工作量混合的最大性能提高58%。我们的方法还将整个集群的资源利用率差异降低了60%。

Linux containers have gained high popularity in recent times. This popularity is significantly due to various advantages of containers over Virtual Machines (VM). The containers are lightweight, occupy lesser storage, have fast boot-up time, easy to deploy and have faster auto-scaling. The key reason behind the popularity of containers is that they leverage the mechanism of micro-service style software development, where applications are designed as independently deployable services. There are various container orchestration tools for deploying and managing the containers in the cluster. The prominent among them are Docker Swarm and Kubernetes. However, they do not address the effects of resource contention when multiple containers are deployed on a node. Moreover, they do not provide support for container migration in the event of an attack or increased resource contention. To address such issues, we propose C-Balancer, a scheduling framework for efficient placement of containers in the cluster environment. C-Balancer works by periodically profiling the containers and deciding the optimal container to node placement. Our proposed approach improves the performance of containers in terms of resource utilization and throughput. Experiments using a workload mix of Stress-NG and iPerf benchmark shows that our proposed approach achieves a maximum performance improvement of 58% for the workload mix. Our approach also reduces the variance in resource utilization across the cluster by 60% on average.

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