Slice-Level Scheduling for High Throughput and Load Balanced LLM Serving

Large language models (LLMs) iteratively generate text token by token, with memory usage increasing with the length of generated token sequences. The unpredictability of generation lengths makes it difficult to estimate the time and memory needed to process requests, posing a challenge for effective request scheduling. Conventional sequence-level scheduling (SLS) serves requests in a first-come first-served (FCFS) manner with static batching where requests with short generation lengths are delayed until those with long ones have finished generation, which hurts computational efficiency. Besides, to avoid out-of-memory (OOM) errors, SLS batches requests with a small batch size, which limits throughput. Recently proposed iteration-level scheduling (ILS) enhances computational efficiency with continuous batching to return completed requests timely and dynamically add new requests for processing. However, many ILS schedulers limit the number of parallel-processing requests to avoid OOM errors while achieving a fast inference speed, which compromises throughput. Moreover, existing SLS and ILS schedulers fail to balance the workload across multiple deployed LLM instances. To tackle these challenges, we propose slice-level scheduling (SCLS). By splitting the predefined maximal generation length limit into slices and serving batches slice by slice, it provides a precise range of serving time and memory usage for batched requests, laying the foundation for effective scheduling. Experiments confirm that compared with SLS and ILS schedulers, SCLS can improve throughput by up to 315.8% and greatly mitigate load imbalance with proposed batching and offloading algorithms.