Retrieval-augmented generation (RAG) improves factuality by conditioning LLMs on retrieved evidence, yet real-world knowledge is often split across tiers: cloud-based RAG can exploit large public corpora, whereas edge-based RAG is the natural place to access private, user-specific stores. This raises a key question: ho...
Yuting Li, Shaoyuan Huang, Xiangqi Liu et al.· Proceedings of the 32nd ACM...· 0 citations
The surge of large language model (LLM) applications on personal devices imposes massive, bursty workloads on cloud serving infrastructure. While prefill-decode disaggregation improves throughput and scalability, memory-bound decode instances often suffer from persistent load imbalance, as output lengths are unknown wh...
Tian-Cheng Zhang, Yulin Chen, Yun-Feng Zhao et al.· 0 citations
Deploying Large Language Models (LLMs) over the edge-cloud continuum faces severe stability challenges due to the conflict between stochastic network topology and complex workflow dependencies. Existing schedulers, relying either on computationally prohibitive Graph Neural Networks (GNNs) or topology-agnostic heuristic...
Yan Gao, Shaoyuan Huang, Yonghui Ye et al.· IEEE Transactions on Cogniti...· 0 citations
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