6 papers
MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Model Serving
Tiancheng Zhang, Yulin Chen, Yunfeng Zhao +3
The surge of large language model (LLM) applications on personal devices imposes massive, bursty workloads on cloud serving infrastructure. While prefill-decode disaggregation impr…
FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads
Tiancheng Zhang, Shaoyuan Huang, Mingyuan Wang +3
Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achieving low latency and high throu…
ACME: Adaptive Customization of Large Models via Distributed Systems
Ziming Dai, Chao Qiu, Fei Gao +2
Pre-trained Transformer-based large models have revolutionized personal virtual assistants, but their deployment in cloud environments faces challenges related to data privacy and…
Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
Xiaofei Wang, Yunfeng Zhao, Chao Qiu +2
Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (E…
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Yunfeng Zhao, Xu Yan, Xiaoqiang Gui +6
Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems,…
Few-Shot Partial-Label Learning
Yunfeng Zhao, Guoxian Yu, Lei Liu +3
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of…