It is possible to frame CPU and GPU utilization as a convex optimization problem if the performance metrics and constraints can be expressed as convex functions. In such cases, resource allocation decisions can be optimized to achieve a globally optimal solution, ensuring robust and efficient performance tuning. However, practical challenges may arise due to nonconvex aspects like discrete scheduling and nonlinear interactions between components. In such instances, approximations or alternative approaches are often necessary to leverage the benefits of convex optimization in modeling and solution strategies.