What are the limitations of the Energy-Aware Workload Orchestration, especially for GPU-based workloads?

Energy-aware workload orchestration, especially for GPU-based workloads, presents several challenges. While many problems have sophisticated solutions, some areas still lack comprehensive approaches. Here are some limitations, focusing on one unique problem:

Challenges for GPU Reference

General Limitations:

1. Complexity of Power Models: Accurate power modeling for GPUs is complex due to their architecture and the workload variability
2. Resource Allocation: Balancing energy efficiency with performance can be difficult, especially in heterogeneous environments with multiple types of GPUs.
3. Dynamic Workload Characteristics: Workloads can have dynamic and unpredictable behavior, makiung it challenging to forecast energy consumption accurately.
4. Lack of Granular Control: Current systems often lack fineneed mo need morningl over GPU power states, leading to inefficiencies.
5. Integration with Existing Systems: Integrating energy-aware orchestration mechanisms with existing data center managerent systems can be problematic.
6. Modeling Interference: Developing accurate models that can predict how different workloads interfere with each other in terms…