In the past few years, the rapid development of information and internet technology has spawned massive amounts of data and information. The explosion of information 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. There are many factors that affect efficiency, such as the idleness of the providers’ cloud computing infrastructure and the additional cost to the clients. One possible new solution is to introduce a comprehensive, win-win negotiation process and schedule resources according to the negotiation results. We propose an auto-negotiation system for resource scheduling based on fuzzy logic in this work. The proposed system is able to complete a one-to-one auto-negotiation process and generate win-win 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 systems. It can be concluded that our proposed system is able to utilize resources more efficiently and is interpretable, highly flexible and customizable. We also successfully train machine learning models to replace the fuzzy negotiation system toimprove processing speed. The thesis also highlights possible future improvements to the proposed systems and machine learning models