Enhancing Machine Learning Performance with AI-Based Virtual Machine Load Balancing

With complex ML models, besides the architecture, there is a strong need for efficient resource management and effective load distribution. Static load balance which was used in the past affects the new form of ML workloads since their dynamics are hard to manage. In this paper, the authors consider the use of intelligent load using AI-Based Virtual Machines in ML and neural networks. These VMs apply features of reinforcement learning and deep learning thereby enhancing the distribution of tasks, reducing latency and improving on fault tolerance. Moreover, AI-driven load balancing also helps to save energy and make the loading balance on cloud and on-premises efficiently. It discusses the effectiveness of using AI -based VM load balancing over the straightforward method as it enhances efficiency of distributing of ML models and executing Inference. There are presented real-world use cases with regard to cloud computing, edge AI, and large neural networks processing. When organizations adopt AI focused automation, they are able to obtain efficient, inexpensive and high performance for their environments for machine learning. The given paper provides evidence that AI-based virtual machine load balancing is a disruptive approach that helps to streamlines the process and significantly speeds up the work of ML models while maintaining high reliability.