Abstract
Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.
Abstract (translated)
边缘-云协同计算（ECCC）已成为解决现代智能应用计算需求的关键范式，通过将云计算资源与边缘设备相结合，实现了高效、低延迟的处理。近年来，在AI领域，尤其是深度学习和大型语言模型（LLMs）方面的进展显著提升了这些分布式系统的性能，但同时也带来了模型部署和资源管理等方面的巨大挑战。本文综述全面考察了分布式智能与模型优化在边缘-云环境中的交汇点，并提供了关于基础架构、赋能技术及新兴应用的系统化教程。此外，我们还详细分析了包括压缩、适应性和神经结构搜索在内的模型优化方法以及驱动AI资源管理策略，这些策略平衡性能、能效和延迟要求。同时探讨了ECCC系统中隐私保护和安全增强的关键方面，并通过自动驾驶、医疗保健及工业自动化等多样化应用的实例来审视实际部署情况。本文还深入探索了性能分析与基准测试技术，以建立对这些复杂系统的评估标准。 此外，综述确定了一系列关键研究方向，包括LLMs部署、6G集成、神经形态计算和量子计算，并提供了应对异构管理、实时处理及可扩展性持续挑战的路线图。通过连接理论进展与实际应用，本调查为研究人员和实践者提供了一个全面视角，利用AI优化分布式计算环境，推动下一代智能系统的创新。 综上所述，本文旨在向读者展示边缘-云协同计算领域中当前的研究趋势、技术进步及未来发展方向，并为该领域的进一步研究提供了宝贵的指导。
URL
https://arxiv.org/abs/2505.01821
