Qwen-Bailian Anonymous Dataset

Overview

This dataset contains a two-hour anonymized KVCache trace of requests sent to a single Qwen model serving instance on Aliyun Bailian. It is used for validating design techniques for LLM serving systems as well as inspiring future usage with the following key workload characteristics collected:

• Temporal distribution of requests;
• Input/output token length;
• Session structure and chat turn patterns;
• Request type composition (text, search, image, file)

Key Features

• Production-Representative: Subset retains real-world traffic patterns
• Privacy-Compliant: Salted hashing + domain remapping anonymization
• Structured Format: JSON Lines with schema documentation
• Apache 2.0 Licensed: Permissive open-source license for commercial use

For insights that can be drawn from the dataset, please refer to our works:

• Optimizing KVCache cache design. (KVCache@ATC'25)

Scenarios behind the traces

• To-C trace, e.g., ChatGPT-like service (./qwen_traceA_blksz_16.jsonl).
• To-B trace, e.g., task automation with API calling (./qwen_traceB_blksz_16.jsonl).

Data Specification of the Traces

Each file contains a representative workload, e.g., qwen_traceB_blksz_16.jsonl refer to a to-B trace collected at 2024.12. Each record contains the following information:

{
  "chat_id": 159,                                   // Randomized chat identifier
  "parent_chat_id": 55,                             // -1 for root requests
  "timestamp": 61.114,                              // Seconds since request arrive
  "input_length": 521,                              // Input token count
  "output_length": 132,                             // Output token count
  "type": "text",                                   // Request type: text/search/image/file
  "turn": 2,                                        // Conversation turn number
  "hash_ids": [1089, 1090, 1091, 6326, ..., 13148]  // Salted SipHash blocks (16 tokens per block)
}

Anonymization Process

1. Token Block Hashing:

   • Group tokens into 16-token blocks
   • Apply salted SipHash-2-4 to each block

2. Domain Remapping:

   • Map hash values to sequential integers
   • Breaks correlation between hash IDs and original content

3. ID Randomization:

   • Replace chat IDs with sequential integers
   • No linkage to user accounts or device identifiers

4. Time-based Anonymization:

   • All timestamps are normalized to trace-relative values, starting from 0 at the beginning of each trace file. Original absolute timestamps (e.g., Unix time) are removed to prevent temporal correlation with external events or user behavior patterns.

Privacy & Compliance

• No PII: All content hashed with irreversible cryptographic functions
• Unlinkable: No cross-session or user-device associations preserved
• GDPR/CCPA Compliant: Meets anonymous data standards under major regulations

License

Apache License 2.0

A permissive license allowing commercial use and modifications, requiring only preservation of the license notice in derivative works

Citation

If you find this dataset useful or use it in your research, please kindly cite our paper using the following bib, thanks!

@inproceedings {kvcache,
  title={KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider},
  author={Wang, Jiahao and Han, Jinbo and Wei, Xingda and Shen, Sijie and Zhang, Dingyan and Fang, Chenguang and Chen, Rong and Yu, Wenyuan and Chen, Haibo}, 
  booktitle = {2025 USENIX Annual Technical Conference (USENIX ATC 25)},
  year = {2025},
  url = {https://www.usenix.org/conference/atc25/presentation/wang-jiahao},
  publisher = {USENIX Association},
  month = jul,
}