Deploying Large Language Models on edge platforms with mobile-oriented resource constraints faces challenges of limited resources and hallucination issues. While Retrieval-Augmented Generation (RAG) mitigates hallucinations through external knowledge, existing RAG systems on such platforms suffer from poor retrieval quality and lack standardized protocols. We propose a RAG framework for edge platforms with mobile-oriented resource constraints based on the Model Context Protocol (MCP), enabling plug-and-play access to heterogeneous knowledge bases. Our framework introduces a weighted voting fusion ranking mechanism integrating BERT-Recall, F1, Relaxed Exact Match (REM), and Query Relevance scores to enhance retrieval accuracy, combined with model quantization and few-shot learning for efficient on-device operation. Experiments on SQuAD, HotpotQA, and TriviaQA demonstrate that our framework achieves higher accuracy and lower error rates than state-of-the-art methods while maintaining low latency.

Cloud computing can help data center consolidate batch and gratis tasks with over-provisioned production applications, and fulfill their diverse resource demands and performance objectives with high scalability and flexibility. One challenge in this hybrid data center is that the dramatic fluctuation of batch and gratis workload may impact performance of production applications, cause task failure, decrease efficiency, and waste computing resources. One way to tackle the challenge is to reduce resource allocation to prevent host overload by delay scheduling tasks if resources are predicted in short. In this paper, we propose hierarchical prediction method for hybrid workload. We use last-state based ARMA model to predict stationary process of production workload, and use feedback based online AR model to predict the vibrated workload of batch and gratis tasks. Evaluation shows that the hierarchical prediction based task scheduling can reduce host overload by more than 85 percent, reduce tasks evicted and killed by more than 60 percent, and reduce 40 percent of average task scheduling delay.