Summary of Ensuring Fair Llm Serving Amid Diverse Applications, by Redwan Ibne Seraj Khan et al.
Ensuring Fair LLM Serving Amid Diverse Applications
by Redwan Ibne Seraj Khan, Kunal Jain, Haiying Shen, Ankur Mallick, Anjaly Parayil, Anoop Kulkarni, Steve Kofsky, Pankhuri Choudhary, Renèe St. Amant, Rujia Wang, Yue Cheng, Ali R. Butt, Victor Rühle, Chetan Bansal, Saravan Rajmohan
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach called FairServe to ensure fair access to large language models (LLMs) in multi-tenant serving platforms. Existing fairness methods do not account for variations in token lengths and multiple LLM calls, making them unsuitable for such platforms. The authors analyze millions of requests from thousands of users on Microsoft’s CoPilot platform, confirming the inadequacy of existing methods. FairServe proposes a system that combines application-characteristic aware request throttling with weighted service counter-based scheduling to curb abusive behavior and ensure fairness. Experimental results demonstrate superior performance compared to state-of-the-art methods in ensuring fairness. The authors are actively working on deploying their system in production, expecting to benefit millions of customers worldwide. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a place where people can ask questions or get answers from computers. Sometimes, some users might use this service too much, making it hard for others to get help. This is unfair! The paper talks about how to make sure everyone gets a fair chance to use these computer services. They studied real data and found that current methods don’t work well because they don’t consider different lengths of questions or requests. The authors created a new system called FairServe that makes it fair for all users. It’s like having a special meter that measures how much each user uses the service, so everyone gets a turn. |
Keywords
* Artificial intelligence * Token
