Cost-efficient negotiation over multiple resources with reinforcement learning

Yu Xu, Jianguo Yao, H. Jacobsen, Haibing Guan
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引用次数: 17

Abstract

Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configuration is large. To this end, we use reinforcement learning to solve the optimization problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously.
通过强化学习在多个资源上进行成本高效的协商
云应用程序可以通过不同的多资源配置实现类似的性能,从而使云服务提供商能够从最佳资源分配中获益,从而降低运营成本。本文旨在从服务水平协议和成本效率两方面考虑,解决多资源协商问题。性能和资源需求通常依赖于应用程序,这使得优化问题变得复杂,特别是当多资源配置的维度很大时。为此,我们利用强化学习来解决多资源配置的优化问题,同时优化学习效率和性能保证。开发的原型名为SmartYARN,扩展了Apache YARN,配备了我们的学习算法,可以使云应用程序经济有效地协商多个资源。广泛的评估表明,SmartYARN在降低资源使用成本的同时保持了对云服务SLA约束的遵从性。
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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