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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Adaptive neuro-fuzzy behavioral learning strategy for effective decision making in the fuzzy-based cloud service negotiation framework
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Adaptive neuro-fuzzy behavioral learning strategy for effective decision making in the fuzzy-based cloud service negotiation framework

机译：基于模糊的云服务谈判框架有效决策的自适应神经模糊行为学习策略

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摘要

Future cloud computing creates a new trend of opting service over the internet through some intelligent third-party broker. In cloud market, both consumer and provider compete with each other against the conflicting requirements, and the competition among cloud providers to trade their services to potential consumers of cloud market. There is an increasing need for automated negotiation framework to quickly reach agreement in competitive cloud market which can provide maximum utility value and success rate among the negotiating parties. Researchers develop various behavioral learning negotiation strategies (such as market driven) in the existing negotiation frameworks for maximizing either the choice of utility value or success rate of parties. Moreover these strategies can be applicable to the environment, where the opponent's behaviors are predictable or precisely known. It may be daunting to apply in the dynamically varying competitive cloud market. So, the proposed Adaptive Neuro-Fuzzy Behavioral Learning (ANFBL) strategy can be applicable, where the opponent's behavior is partially and imprecisely known. Therefore, the proposed strategy can maximize both utility value and success rate without compromising either choice. An extensive simulation is conducted to evaluate the efficiency of strategies which shows that proposed strategy achieve higher utility and higher success rate than existing learning approach, without any negotiation conflict among the parties.
机译：未来的云计算通过一些智能的第三方经纪人创造了在互联网上进行服务的新趋势。在云市场中，消费者和提供商彼此竞争对抗冲突的要求，以及云提供商之间的竞争，将他们的服务交换到云市场的潜在消费者。越来越需要自动谈判框架，以便在竞争激烈的云市场中快速达到协议，这可以在谈判方之间提供最大的效用价值和成功率。研究人员在现有的谈判框架中制定各种行为学习谈判策略（如市场驱动），以最大限度地提高缔约方的实用价值或成功率的选择。此外，这些策略可以适用于环境，在那里对手的行为是可预测的或精确的众所周知的。在动态不同的竞争性云市场中应用可能是令人生畏的。因此，所提出的自适应神经模糊行为学习（ANFBL）策略可以适用，其中对手的行为是部分和不切实义的。因此，拟议的策略可以最大限度地提高效用价值和成功率，而不会影响任何一种选择。进行了广泛的模拟，以评估策略的效率，表明拟议的策略实现了比现有的学习方法更高的效用和更高的成功率，没有各方之间的任何谈判冲突。

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