ashman1705/LLMScheduling

ShareGPT Workload Cases

This dataset packages workload-generation artifacts derived from Aeala/ShareGPT_Vicuna_unfiltered. It includes the manifest describing how cases were generated and per-category case files intended for scheduler and systems benchmarking.

Contents

• —manifest.json: dataset generation metadata and category definitions.
• —cases/: 176 generated case files across 22 workload categories.

Generation Metadata

• —Split: train
• —Cases per category: 8
• —Tokenizer: gpt-4 via tiktoken
• —Generation seed: 7

Category Preview

• —steady_short: Steady Poisson arrivals dominated by short prompts. (32 requests per case)
• —steady_mixed: Steady mixed load with balanced request lengths. (64 requests per case)
• —bursty_chat: Bursty conversational traffic with mostly short and medium prompts. (96 requests per case)
• —bursty_long: Burst-heavy load with a larger share of long prompts. (72 requests per case)
• —flash_crowd: Large clustered traffic spikes for stress testing scheduler fairness and queueing. (192 requests per case)
• —uniform_workload: Uniform request sizes and steady arrivals for baseline throughput and scheduler overhead. (64 requests per case)
• —low_load: Low-arrival workload with little contention to approximate ideal latency. (32 requests per case)
• —short_long_mix: Majority short requests with a minority of long requests to expose head-of-line blocking. (80 requests per case)
• —...and 14 more categories.

Notes

• —Each category folder also contains a category.json file with category-level metadata.
• —Source records are intentionally excluded from this Hub upload.
• —The uploaded layout mirrors the local directory structure for reproducibility.