NCA-AIIO Question 102
Select 3A company is managing an AI cluster using Kubernetes and wants to optimize job scheduling to ensure both high GPU utilization and fairness across multiple teams running workloads. Which of the following are essential considerations when setting up AI cluster orchestration and job scheduling?
- A
Implementing a resource quota system to allocate GPU resources across teams
- B
Using a round-robin scheduling policy to assign GPU jobs
- C
Configuring node affinity and anti-affinity rules for workload placement
- D
Prioritizing job scheduling based solely on job size and runtime requirements
- E
Leveraging GPU-aware scheduling to ensure workloads are placed on nodes with available GPUs
Show answer and explanation
Correct answers: A, C, E
Explanation
Efficient AI cluster orchestration and job scheduling require balancing utilization and fairness. Resource quotas ensure fair allocation of GPUs across teams, while node affinity and GPU-aware scheduling enable efficient resource placement and usage. Round-robin scheduling and simplistic job prioritization approaches often fail to address the complexity of AI workloads, making them less suitable for AI cluster management.
- A. Correct.
Implementing a resource quota system ensures equitable distribution of GPU resources among teams, which is critical for maintaining fairness in a shared AI cluster environment.
- B. Incorrect.
Round-robin scheduling is not ideal for GPU workloads as it does not take resource availability or workload requirements into account, which can lead to inefficient utilization.
- C. Correct.
Node affinity and anti-affinity rules help control where workloads are placed, ensuring better resource utilization and minimizing conflicts between jobs.
- D. Incorrect.
Scheduling solely on job size and runtime requirements may ignore critical factors such as GPU availability, leading to suboptimal resource allocation.
- E. Correct.
GPU-aware scheduling is necessary to ensure jobs that require GPUs are placed on nodes with available GPU resources, optimizing utilization and performance.