Extend the JBenchmark system to support structured benchmarking across multiple llm-d configuration scenarios. This task aims to evaluate how different llm-d runtime settings affect model performance, stability, and resource efficiency when integrated with GuideLLM.

This includes the ability to:

• Benchmark under different router configurations (e.g., batching strategy, latency targets)

• Evaluate various Placement/Dispatch (P/D) strategies (e.g., static vs. dynamic node selection, GPU/resource awareness)

• Run over different GuideLLM datasets to simulate a variety of enterprise use cases

• Capture rich metadata for every benchmark run to enable reproducible comparisons

Examples of Configurable Parameters:

• Router strategies (e.g., round-robin, latency-aware)

• Batching configurations and max concurrency

• P/D policies and node-affinity rules

• Dataset variability (prompt types, lengths, formats)

Acceptance Criteria:

• Benchmark runs can be parameterized with different llm-d settings

• All runs are tagged with full config metadata

• Results are stored, queryable, and comparable via the benchmarking dashboard

• Significant configuration impacts are highlighted in reports for GuideLLM/llm-d stakeholders