Large Language Model for Practical Evaluation in Cloud Computing with Applications of Real World Data

This study revolves around Large Language Models (LLMs) within the confines of the recent cloud computing setup, particularly proliferation control and management of resources. The work employs MDPs & Bayesian inference to bring about high accuracy rates in forecasts as well as action productivity. Data obtained from major cloud services providers (Azure, AWS, GCP, Oracle & IBM) indicated improvements in disk I/O performance, network and memory consumption latency and CPU processing capabilities. This study was able to show that LLM outperforms traditional models by enhancing job scheduling load and reduction of queuing time. Bayesian inference contributed in adjusting the estimates for the resources to be used while MDPs offered a structured approach to reactive improvement so as to minimize delay and enhance operational performance. LLMs are expected to be a cornerstone in the future management of cost, reliability & performance of cloud based services. Further research should be directed towards object based storage architecture, unknown corruption and privacy problems of using artificial intelligence in a cloud environment.