提高运行混合工作负载的大规模云系统的成本效益 - 阿里巴巴集群轨迹的案例研究
Brad Everman1, Narmadha Rajendran1, Xiaomin Li1
1Texas State University, San Marcos, TX, United States.
概括
在数据中心巩固电子商务和批量工作,提高了云系统的利用率. 这一策略降低了所有权总成本 (TCO) 并提高了性能,为大规模的云运营提供了显著的节省.
科学领域:
- 云计算 云计算 云计算 云计算
- 数据中心优化数据中心优化
- 电子商务系统 电子商务系统
背景情况:
- 由于COVID-19大流行加速了电子商务的增长,对强大的云服务的需求增加了.
- 数据中心面临的挑战是支持混合工作负载 (在线零售,批处理) 以高效和成本效益的方式.
- 优化系统利用是平衡高性能和降低总拥有成本 (TCO) 的关键.
研究的目的:
- 分析阿里巴巴的集群痕迹 (2017年,2018年) 以了解大规模云系统性能和成本效益.
- 识别和解决云环境中资源过度订阅和不足订阅的问题.
- 评估混合工作负载的整合策略,以提高系统利用率和TCO.
主要方法:
- 对阿里巴巴2017年和2018年的集群痕迹进行了全面分析.
- 开发一个模拟器来评估工作负载整合解决方案.
- 实验评估对性能,能源消耗和TCO的影响.
主要成果:
- 在阿里巴巴的云系统中发现了共存的超额订阅和不足订阅问题.
- 对于2018年轨迹 (4,000+台机器) 证明了潜在的TCO降低60万美元.
- 如果适用于阿里巴巴的所有面向网络的服务器,预计TCO减少将近6800万美元.
结论:
- 将时间敏感的在线工作与时间不敏感的批量工作结合起来是一个有效的策略.
- 这种方法显著提高了大规模数据中心的系统利用率,性能和成本效益.
- 这些发现为优化云基础设施和降低运营成本提供了一个可扩展的模型.
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