In the past few decades, the rapid development of information and internet
technologies has spawned massive amounts of data and information. The
information explosion drives many enterprises or individuals to seek to rent
cloud computing infrastructure to put their applications in the cloud. However,
the agreements reached between cloud computing providers and clients are often
not efficient. Many factors affect the efficiency, such as the idleness of the
providers' cloud computing infrastructure, and the additional cost to the
clients. One possible solution is to introduce a comprehensive, bargaining game
(a type of negotiation), and schedule resources according to the negotiation
results. We propose an agent-based auto-negotiation system for resource
scheduling based on fuzzy logic. The proposed method can complete a one-to-one
auto-negotiation process and generate optimal offers for the provider and
client. We compare the impact of different member functions, fuzzy rule sets,
and negotiation scenario cases on the offers to optimize the system. It can be
concluded that our proposed method can utilize resources more efficiently and
is interpretable, highly flexible, and customizable. We successfully train
machine learning models to replace the fuzzy negotiation system to improve
processing speed. The article also highlights possible future improvements to
the proposed system and machine learning models. All the codes and data are
available in the open-source repository.

提出了一种基于模糊逻辑的基于代理的自动协商资源调度系统，通过引入综合性协商游戏，根据协商结果对资源进行调度，从而达到更高效地利用资源的目的，并成功使用机器学习模型替代模糊协商系统以提高处理速度。