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This study evaluates the performance of an AI/ML-powered dispatch agent tasked with managing scientific workflows across heterogeneous multi-cluster environments. By conducting 432 runs and exploring various feature dimensions and prompt styles, the agent achieved a remarkable 97.9% success rate in job dispatching. Notably, the introduction of descriptive metadata significantly enhanced job execution success from 48% to 87%, while also improving performance metrics for several applications by up to 3.3 times.
Descriptive metadata can boost job execution success rates in multi-cluster AI workflows from 48% to 87%, revolutionizing operational efficiency.
Agents powered by AI/ML are becoming ingrained in orchestration. Dispatch of work is the task of receiving a request, transforming it for a workload manager, and successfully submitting it. Running scientific workflows across multi-cluster environments introduces substantial challenges of dynamic job transformation, dispatch, and submission to heterogeneous clusters. These tasks are well-suited to agents, which can receive textual instructions for work, prepare job specifications, and dispatch. In this work, we assess the reliability of a dispatch agent across 432 runs, testing all possible combinations of five feature dimensions across four prompt styles. The agent is highly reliable (97.9% success). We test a full orchestration to submit, queue, match, score, select, transform, and dispatch in a multi-cluster experiment. We find that descriptive metadata increases successful execution from 48% to 87% of 220 submitted jobs, eliminating architecture mismatch, and improving performance for five of ten measurable applications by up to 3.3x.
