VQV Signal
Current LLM inference scheduling algorithms often assume steady Poisson request arrivals, which do not capture the bursty and dynamic nature of real-world traffic. This work proposes modifications to the WAIT algorithm to better handle bursty workload distributions, aiming to improve throughput whi...
Current LLM inference scheduling algorithms often assume steady Poisson request arrivals, which do not capture the bursty and dynamic nature of real-world traffic. This work proposes modifications to the WAIT algorithm to better handle bursty workload distributions, aiming to improve throughput whi...
AI-assisted summary based on the listed source.
Large Language Models (LLMs) such as ChatGPT and Claude are widely used for information retrieval and problem-solving. Recent work has focused on improving scheduling algorithms to boost throughput while maintaining low latency. However, these approaches often assume Poisson request arrivals with constant rates -...
Accurately modeling and managing bursty request patterns is crucial for optimizing LLM inference performance in practical deployments. Enhancing scheduling algorithms to reflect real-world traffic can lead to more efficient resource use and better user experience.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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