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Question 83/89
You are responsible for managing an AI infrastructure that includes multiple GPU clusters for deep learning workloads. One of your tasks is to efficiently allocate resources and manage workloads across these clusters using an orchestration platform. Which of the following approaches would best optimize the utilization of GPU resources while ensuring high availability of the AI workloads?
Correct Answer: C
Implementing a load-balancing algorithm that dynamically assigns workloads based on real-time GPU availability is the best approach to optimize resource utilization and ensure high availability in multi-cluster GPU environments. This method, supported by NVIDIA's "DeepOps" and Kubernetes with GPU Operator, monitors GPU metrics (e.g., utilization, memory) via tools like DCGM and allocates workloads to underutilized clusters, preventing bottlenecks and ensuring failover. This dynamic approach adapts to workload changes, maximizing efficiency and uptime.
Round-robin (A) and FCFS (D) ignore real-time resource states, leading to inefficiency. Static scheduling (B) lacks adaptability. NVIDIA's orchestration guidelines favor dynamic load balancing for AI clusters.
Round-robin (A) and FCFS (D) ignore real-time resource states, leading to inefficiency. Static scheduling (B) lacks adaptability. NVIDIA's orchestration guidelines favor dynamic load balancing for AI clusters.
- Question List (89q)
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