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首页> 外文期刊>International Journal of Cloud Computing >Learning techniques for cloud demands aggregation in cloud service brokering
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Learning techniques for cloud demands aggregation in cloud service brokering

机译：云学习技术要求云服务代理中的聚合

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摘要

The cloud service broker (CSB) has to ensure intermediation for cloud services. Especially, it acts as an interface among customers and providers of cloud resources. The main purpose of the CSB is to pertinently aggregate different demands of customers into a global demand. The objective is to obtain a price for this global demand as low as possible from the providers. Finding the best aggregations of demands is an optimisation problem. In a competitive context between providers, this optimisation problem has to be solved taking into account partial knowledge. We model this problem as a game in which we evaluate the quality of Nash equilibria from the optimisation problem point of view. We propose to use a linear reward inaction algorithm to reach such equilibria in a partial knowledge and distributed context after proving a convergence property of the algorithm. This approach is finally experimented by numerous simulations.
机译：云服务代理（CSB）必须确保云服务的中介。特别是，它充当云资源的客户和提供商之间的接口。 CSB的主要目的是将不同的客户需求汇总到全球需求中。目的是从供应商那里获得尽可能低的全球需求价格。找到需求的最佳集合是一个优化问题。在提供商之间竞争的情况下，必须考虑部分知识来解决此优化问题。我们将此问题建模为一个博弈，从优化问题的角度评估纳什均衡的质量。在证明算法的收敛性后，我们建议使用线性奖励不作为算法在部分知识和分布式上下文中达到这种平衡。最终，该方法已通过大量仿真实验。

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