AUTOMATED REASONING AND INFERENCE FOR CLOUD-BASED

Abstract

Argumentation and inference is a procedure for understanding the problem and solving it in the machine. Here, the machine is meant to be any computer system that depends on artificial intelligence to understand the environment and interact with it. One of important issue in the cloud-based systems that are seen in all of them is load balancing. Load balance is an important and challenging problem in cloud computing. Our purpose in this study is to consider the two important parameters of the processor efficiency of the node and the time delay between the order and the server in terms of geographical distance, using automated Fuzzy Inference Intelligence argumentation and inference, we have the advantage of using uncertain inputs, and we can maximize system performance for clouds with different sizes and achieve better load balances. We first have some related research to determine the innovation of the present study. The results show that using fuzzy argument and fuzzy inference, we have been able to significantly reduce the response time and also the results indicate that the distance between the customer and the destination node is the parameter that has the most effect on the efficiency of the proposed model

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