FIFE:一个基于基础架构即代码的框架,用于评估来自多云的VM实例

Yuhui Lin, J. Briggs, A. Barker
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引用次数: 1

摘要

为了选择最佳的虚拟机,云用户通常需要通过基准测试或运行黑盒搜索技术(如贝叶斯优化)来评估虚拟机的性能。为了促进这一过程,我们开发了一个通用的、高度可配置的框架,支持虚拟机评估(FIFE)的基础设施即代码(IaC)。FIFE将进程抽象为搜索器、选择器、部署器和解释器。它允许用户使用JSON指定目标VM集和评估目标,以实现流程自动化。我们通过建立一个贝叶斯优化虚拟机搜索系统来演示该框架的使用。我们用不同的实验设置来评估系统,即云提供商编号和并行搜索的不同组合。结果表明,当搜索空间由来自多个云提供商的VM组成时,搜索效率保持不变,并且在适当设置并行化数的情况下,并行搜索可以显著减少搜索时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FIFE: an Infrastructure-as-Code Based Framework for Evaluating VM Instances from Multiple Clouds
To choose an optimal VM, Cloud users often need to step a process of evaluating the performance of VMs by benchmarking or running a black-box search technique such as Bayesian optimisation. To facilitate the process, we develop a generic and highly configurable Framework with Infrastructure-as-Code (IaC) support For VM Evaluation (FIFE). FIFE abstract the process as a searcher, selector, deployer and interpreter. It allows users to specify the target VM sets and evaluation objectives with JSON to automate the process. We demonstrate the use of the framework by setting up of a Bayesian optimization VM searching system. We evaluate the system with various experimental setups, i.e. different combinations of cloud provider numbers and parallel search. The results show that the search efficiency remains the same for the case when the search space is consist of VM from multiple cloud providers, and the parallel search can significantly reduce search time when the number of parallelisation is set properly.
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