Utilizing LLMs for Virtual Machine Migration in Cloud Computing Environments

The current methods for virtual machine migration operate through inflexible decision systems, which prevent system adaptability and reliable pattern detection. The research develops a theoretical framework which demonstrates how large language models (LLMs) can create an adaptive migration system that understands its environment. The framework consists of three distinct sections, where the first monitors resources and data in real-Time and the second section predicts workload patterns, and the third section optimizes migration decisions. The layered system design according to initial modelling results in better resource utilization by 20-30% and faster decision-making at 50-200 ms with 85-95% accurate predictions compared to conventional methods. The promising results from these projections highlight both the benefits and difficulties that come with real-Time processing and protecting confidential data. The proposed framework establishes a strong foundation to transform virtual machine migration approaches while enabling the creation of intelligent cloud resource management systems.