Which job scheduling strategy would best optimize resource utilization and reduce job queuing times in a large-scale AI cluster?

The strategy of implementing preemptive scheduling for high-priority jobs effectively optimizes resource utilization and reduces job queuing times in a large-scale AI cluster. This method allows more critical tasks that demand immediate attention to interrupt and take precedence over lower-priority jobs. By doing this, the system can minimize latencies for urgent AI workloads, which is often necessary in environments where timely processing is critical, such as in real-time data analysis or model training.

Preemptive scheduling is beneficial in balancing resources among various jobs by ensuring that high-priority tasks are completed promptly, which can lead to a more efficient overall throughput in the system. This approach helps prevent bottlenecks that often arise when lower-priority tasks occupy resources excessively, thus allowing resources to be reallocated dynamically. As a result, preemptive scheduling not only enhances responsiveness for high-priority jobs but also helps maintain a steady flow in job execution, ultimately reducing queuing times for all jobs in the queue.

Other strategies may not address the dynamic nature of AI workloads as effectively. For example, using a first-come, first-served scheduling policy might lead to longer wait times for critical jobs, while scheduling based on estimated runtime could either overestimate or underestimate job needs, causing inefficient resource