私有云计算基础设施多节点部署方式的OpenStack实现

H. T. Ciptaningtyas, K. Ghozali, Ridho Rahman Hariadi, R. W. Sholikah, M. Husni, I. Made, Dindra Setyadharma
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引用次数: 0

摘要

到2026年,仅云计算的全球市场预计将达到9473亿美元。OpenStack技术为构建私有云创造了机会。it部ITS使用OpenStack创建自己的私有云,采用Kolla Ansible开发的多节点部署机制。通过使用Phoronix Test Suite、Ethr和Rally-Openstack工具来评估OpenStack实现的结果,以确定实例计算性能与运行实例数量之间的相关性,以及在生成实例和改变风格时OpenStack服务性能之间的相关性。结果表明,随着实例数量的增加,每个实例的CPU性能会下降,平均下降0.68 GFLOPS或1.41%。每个实例的内存性能将下降,平均下降0.37 GBps或4.46%。实例创建的持续时间不受并发请求数量的影响,仅增加0.3秒。同时请求的数量也会影响风味变化的时间。风格变化的持续时间可以是一个并发请求的2倍(在4个并发请求中),但是在5个或更多并发请求中会经历不稳定。KYPO Cyber Range平台利用Openstack的10个vcpu和14.3 GB内存资源,分配一个沙盒池来运行训练,成功实现了一个线性训练演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OpenStack Implementation using Multinode Deployment Method for Private Cloud Computing Infrastructure
The worldwide market for cloud computing alone is expected to reach $\mathrm{US}{\$}947.3$ billion by 2026. OpenStack technology creates opportunities to build private clouds. Department of Information Technology ITS creates its own private cloud using OpenStack and employs the multinode deployment mechanism developed by Kolla Ansible. The OpenStack implementation's results were evaluated by using the Phoronix Test Suite, Ethr, and Rally-Openstack tools to identify the correlation between instance computing performance and the number of running instances, as well as OpenStack service performance while generating instances and changing flavors. The results show that the CPU performance of each instance will decrease with an average decrease of 0.68 GFLOPS or 1.41% as the number of instances increases. The memory performance of each instance will decrease with an average reduction of 0.37 GBps or 4.46%. The duration of the instance creation is unaffected by the number of concurrent requests, with an increase of only 0.3 seconds. The number of simultaneous requests also affects the time of flavor changes. The duration of flavor changes can be up to 2 times (in 4 concurrent requests) from one concurrent request but experiences instability in 5 or more concurrent requests. The KYPO Cyber Range Platform successfully implemented a linear training demo by allocating one sandbox pool to run the training using Openstack resources of 10 vCPUs and 14.3 GB of memory.
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