Publications

2027

1. EuroSys27

   Valve: Risk-Bounded Offline Execution in Online Generative Serving

   Yanying Lin , Haiying Shen , Shijie Peng, and 6 more authors

   In Proceedings of the 22nd European Conference on Computer Systems, 2027

   Bib

   @inproceedings{lin2027valve,
     title = {Valve: Risk-Bounded Offline Execution in Online Generative Serving},
     author = {Lin, Yanying and Shen, Haiying and Peng, Shijie and Lu, Chengzhi and Ma, Chong and Shen, Min and Xu, Chengzhong and Qu, Lin and Ye, Kejiang},
     booktitle = {Proceedings of the 22nd European Conference on Computer Systems},
     year = {2027},
   }

2026

1. ICPP

   ReliefServe: Relieving GPU Pressure in Multi-Model Serving via Selective CPU Escape

   Shijie Peng , Yanying Lin , Chengzhi Lu, and 3 more authors

   In Proceedings of the 55th IEEE International Conference on Parallel Processing, 2026

   DOI Bib HTML PDF

   @inproceedings{peng2026reliefserve,
     title = {ReliefServe: Relieving GPU Pressure in Multi-Model Serving via Selective CPU Escape},
     author = {Peng, Shijie and Lin, Yanying and Lu, Chengzhi and Wu, Shuaipeng and Xu, Chengzhong and Ye, Kejiang},
     booktitle = {Proceedings of the 55th IEEE International Conference on Parallel Processing},
     year = {2026},
     doi = {10.1145/3832810.3832827},
   }

2. EuroSys

   FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters

   Yanying Lin , Shijie Peng , Chengzhi Lu, and 2 more authors

   In Proceedings of the 21st European Conference on Computer Systems, 2026

   Insight Bib HTML PDF

   Insight: We find that for pipeline inference, the optimal online pipeline structure varies greatly depending on the incoming request distribution. Longer pipelines can absorb load spikes by dispersing demand, while shorter pipelines reduce communication overhead. Dynamically adjusting the pipeline structure according to the request distribution is an effective approach in serverless environments.

   @inproceedings{lin2026flexpipe,
     title = {FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters},
     author = {Lin, Yanying and Peng, Shijie and Lu, Chengzhi and Xu, Chengzhong and Ye, Kejiang},
     booktitle = {Proceedings of the 21st European Conference on Computer Systems},
     year = {2026},
   }

3. TPDS

   Workload-Adapted Resource Allocation for LLM Distributed Serving in Serverless Clusters

   Yanying Lin , Shijie Peng , Yanbo Li, and 4 more authors

   IEEE Transactions on Parallel and Distributed Systems, 2026

   Bib

   @article{lin2026workload,
     title = {Workload-Adapted Resource Allocation for LLM Distributed Serving in Serverless Clusters},
     author = {Lin, Yanying and Peng, Shijie and Li, Yanbo and Luo, Shutian and Shen, Haiying and Ye, Kejiang and Xu, Chengzhong},
     journal = {IEEE Transactions on Parallel and Distributed Systems},
     year = {2026},
   }

2025

1. Cluster

   ROCK: Serving Multimodal Models in Cloud with Heterogeneous-Aware Resource Orchestration for Thousands of LoRA Adapters

   Shuaipeng Wu , Yanying Lin , Shijie Peng, and 6 more authors

   In Proceedings of the 2025 IEEE International Conference on Cluster Computing, 2025

   Insight Bib PDF

   Insight: This work analyzes the performance of different request workloads in production-level environments and proposes a heterogeneous graph matching algorithm to adapt to different levels of resource characteristics and inference performance of various workloads. ROCK is a system-level resource orchestration framework designed to optimize the deployment of thousands of LoRA adapters in a cloud environment through intelligent resource allocation and workload-aware scheduling.

   @inproceedings{wu2025rock,
     title = {ROCK: Serving Multimodal Models in Cloud with Heterogeneous-Aware Resource Orchestration for Thousands of LoRA Adapters},
     author = {Wu, Shuaipeng and Lin, Yanying and Peng, Shijie and Chen, Wenyan and Ma, Chong and Shen, Min and Chen, Le and Xu, Chengzhong and Ye, Kejiang},
     booktitle = {Proceedings of the 2025 IEEE International Conference on Cluster Computing},
     year = {2025},
   }

2. IEEE TSC

   Serving LLM in Distributed GPU Cluster With Fine-Grain Pipeline Constraints

   Yanying Lin , Shijie Peng , Shuaipeng Wu, and 4 more authors

   IEEE Transactions on Services Computing, 2025

   Insight Bib

   Insight: LLMs are increasingly deployed in distributed GPU clusters using pipeline parallelism for efficient inference. However, existing approaches treat the entire pipeline as a monolithic unit, resulting in coarse-grained resource management and suboptimal SLO compliance. Each pipeline stage has distinct workload characteristics, but current systems cannot leverage this for better performance control. We propose fine-grained pipeline constraints that manage each stage independently, enabling precise SLO allocation at the stage level.

   @article{lin2025serving,
     title = {Serving LLM in Distributed GPU Cluster With Fine-Grain Pipeline Constraints},
     author = {Lin, Yanying and Peng, Shijie and Wu, Shuaipeng and Li, Yanbo and Lu, Chengzhi and Ye, Kejiang and Xu, Chengzhong},
     journal = {IEEE Transactions on Services Computing},
     year = {2025},
     volume = {18},
     number = {5},
   }

3. IEEE TCC

   Understanding Serverless Inference in Mobile-Edge Networks: A Benchmark Approach

   Junhong Chen , Yanying* Lin , Shijie Peng, and 5 more authors

   IEEE Transactions on Cloud Computing, 2025

2024

1. ICDCS

   QUART: Latency-Aware FaaS System for Pipelining Large Model Inference

   Yanying Lin , Yanbo Li , Shijie Peng, and 5 more authors

   In Proceedings of the 44th IEEE International Conference on Distributed Computing Systems, 2024

   Insight Bib PDF

   Insight: We find that when using pipeline inference for LLMs, each stage exhibits significant load differences due to varying resource allocation and throughput across different workloads. For example, under stable or bursty traffic patterns, throughput disparities between upstream and downstream stages can create performance-critical bottlenecks in the pipeline.

   @inproceedings{lin2024quart,
     title = {QUART: Latency-Aware FaaS System for Pipelining Large Model Inference},
     author = {Lin, Yanying and Li, Yanbo and Peng, Shijie and Tang, Yingfei and Luo, Shutian and Shen, Haiying and Xu, Chengzhong and Ye, Kejiang},
     booktitle = {Proceedings of the 44th IEEE International Conference on Distributed Computing Systems},
     year = {2024},
   }

2. ICWS

   Planck: Optimizing LLM Inference Performance in Pipeline Parallelism with Fine-Grained SLO Constraint

   Yanying Lin , Shijie Peng , Shuaipeng Wu, and 4 more authors

   In Proceedings of the 31st IEEE International Conference on Web Services, 2024

   Insight

   Insight: This work proposes preliminary ideas for SLO allocation in LLM pipeline parallelism. We explore fine-grained SLO constraint mechanisms to optimize inference performance by intelligently distributing service level objectives across different pipeline stages, enabling more efficient resource utilization and better meeting diverse application requirements.

3. CSCWD

   EINS: Edge-Cloud Deep Model Inference with Network-Efficiency Schedule in Serverless

   Shijie Peng , Yanying Lin , Wenyan Chen, and 3 more authors

   In Proceedings of the 27th International Conference on Computer Supported Cooperative Work in Design, 2024

   Bib

   @inproceedings{peng2024eins,
     title = {EINS: Edge-Cloud Deep Model Inference with Network-Efficiency Schedule in Serverless},
     author = {Peng, Shijie and Lin, Yanying and Chen, Wenyan and Tang, Yingfei and Duan, Xu and Ye, Kejiang},
     booktitle = {Proceedings of the 27th International Conference on Computer Supported Cooperative Work in Design},
     year = {2024},
   }

2023

1. ICPADS

   FLASH: Low-Latency Serverless Model Inference with Multi-Core Parallelism in Edge

   Yanbo Li , Yanying* Lin , Shijie Peng, and 4 more authors

   In Proceedings of the 29th IEEE International Conference on Parallel and Distributed Systems, 2023