Data centers face the complex challenge of efficiently allocating processing jobs, like fitting Tetris blocks. Virtual machine (VM) lifespans are uncertain, making allocation difficult. Google's LAVA system aims to improve efficiency using AI to predict VM lifetimes. Unlike single predictions, LAVA uses "continuous reprediction," constantly updating lifespan estimates. This involves a learned probability distribution to account for varying VM behaviors. The system includes three algorithms: NILAS, which incorporates lifetime predictions to optimize host selection. LAVA places shorter-lived VMs with longer-lived ones, adapting to mispredictions. LARS minimizes VM disruptions during maintenance based on predicted lifespans. The model is integrated directly into the scheduler for low latency and high reliability. NILAS has shown significant improvements, increasing empty hosts and reducing resource stranding. Simulations suggest LAVA and LARS will further boost efficiency. The project demonstrates the successful integration of machine learning for data center optimization.