Cloud resource orchestration optimisation based on ARIMA

Hua Qin, Min Yu, Yingxu Lai, Zenghui Liu, Jing Liu
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Abstract

The problem of resources management in the cloud environment, focusing on the platform as a service (PaaS), satisfying the demands of users, and relieving the load on the server in high concurrency, requires attention. After analysing the resource orchestration technology on PaaS, this paper presents a dynamic orchestration optimisation framework based on autoregressive integrated moving average model (ARIMA) model. The architecture is based on an OpenStack infrastructure as a service (IaaS), and combines with Cloudify, a resource orchestration software on the PaaS. Adjustments may be made in advance by predicting the resource consumption in the next time period. Experiments showed that this architecture can effectively shorten the concurrent response time and improve the utilisation of memory.
基于ARIMA的云资源编排优化
云环境下的资源管理问题,以平台即服务(platform as a service, PaaS)为核心,满足用户的需求,并在高并发的情况下减轻服务器的负载,是需要关注的问题。在分析了PaaS资源编排技术的基础上,提出了一种基于自回归综合移动平均模型(ARIMA)模型的动态编排优化框架。该架构基于OpenStack的基础设施即服务(IaaS),并结合了PaaS上的资源编排软件Cloudify。通过预测下一时间段的资源消耗情况,可以提前进行调整。实验表明,该架构能有效缩短并发响应时间,提高内存利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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