摘要(Abstract)

智能ECN(Edge Computing Node)兼容多种异构联接、支持实时处理与响应、提供软硬一体化安全等； 边缘计算参考架构在每层提供了模型化的开放接口，实现了架构的全层次开放；边缘计算参考架构通过纵向管理服务、数据全生命周期服务、安全服务，实现业务的全流程、全生命周期的智能服务。 相关应用 云卸载：在传统的内容分发网络中，数据都会缓存到边缘结点。随着物联网的发展，数据的生产和消费都是在边缘结点，也就是说边缘结点也需要承担一定的计算任务。把云中心的计算任务卸载到边缘结点这个过程叫做云卸载。 视频分析 智慧城市：对基于位置的一些应用来说，边缘计算的性能要由于云计算。比如导航，终端设备可以根据自己的实时位置把相关位置信息和数据交给边缘结点来进行处理，边缘结点基于现有的数据进行判断决策。整个过程中的网络开销都是最小的。用户请求得以极快的得到响应。 智能家居。。。 相关技术 边缘协作：利用多个边缘结点协同合作，创建一个虚拟的共享数据的视图，利用一个预定义的公共服务接口来将这些数据进行整合。同时通过这个接口，我们可以编写应用程序为用户提供更复杂的服务 --------------------- 作者：EmilyGnn 来源：CSDN 原文：https://blog.csdn.net/gaoruowen1/article/details/82780073 版权声明：本文为博主原创文章，转载请附上博文链接！


主题(Topic)
learning ECN 固件 性能 Firmware Performance Component 构件
adversarial-reinforcement-learning 概念模型 Conceptual model
stc89c51 remote-workers Rascal easydss monorepo ui-components room microservices-architecture flashing cmf redundancy col cpp wepay hls-server roundbutton popupdialog dagger2-mvp-rxjava esp-12f uniapp ssdp php-webshells openapi appveyor Kotlin cheatsheet jqueryui neumorphism prototeam dex djy yuque gpt2 dns-proxy laravel54 android-material imageloader fast-build node-proxy work-from-home imageeditor anime subdomian-find

adversarial-learning reinforcement-learning reinforcement-learning- adversarial-active-learning adversarial-feature-learning adversarial-machine-learning adversarial-reward-learning adversarial-transfer-learning graph-adversarial-learning applied-reinforcement-learning berkeley-reinforcement-learning carla-reinforcement-learning causal-reinforcement-learning checkers-reinforcement-learning constrained-reinforcement-learning continuous-reinforcement-learning coursera-reinforcement-learning curriculum-reinforcement-learning deep-reinforcement-learning dissecting-reinforcement-learning distributed-reinforcement-learning feudal-reinforcement-learning hierachical-reinforcement-learning hierarchical-reinforcement-learning interactive-reinforcement-learning inverse-reinforcement-learning meta-reinforcement-learning minecraft-reinforcement-learning multiagent-reinforcement-learning munchausen-reinforcement-learning practical-reinforcement-learning pytorch-reinforcement-learning recurrent-reinforcement-learning reinforcement-learning-agent reinforcement-learning-agents reinforcement-learning-alberta reinforcement-learning-algorithm reinforcement-learning-algorithms reinforcement-learning-analysis reinforcement-learning-books reinforcement-learning-bot reinforcement-learning-bots reinforcement-learning-cartpole reinforcement-learning-concepts reinforcement-learning-datasets reinforcement-learning-environ reinforcement-learning-environment reinforcement-learning-environments reinforcement-learning-examples
项目(Project)
PavelCz/rl-adversarial-attack ahq1993/inverse_rl davide97l/rl-policies-attacks-defenses SaminYeasar/Off_Policy_Adversarial_Inverse_Reinforcement_Learning asokraju/Adv-MARL Johnny-Zhang92/IRL-Essential-Code huanzhang12/SA_DDPG huanzhang12/SA_PPO decisionforce/MAIRL LinZichuan/AdMRL chenhongge/SA_DQN chenhongge/StateAdvDRL awjuliani/DeepRL-Agents awesome-reinforcement-learning-zh XueyingBai/Model-Based-Reinforcement-Learning-for-Online-Recommendation konichuvak/rl_experiments vishwapardeshi/AI-Pacman-Projects mbs0221/Adversarial-DL Yashwanthv21/Reinforcement-Learning sergeLabo/balance singhgaurav2323/reinforcement simerplaha/reinforcement-learning levimcclenny/Reinforcement_Learning Issuenate/RL-Policy-Gradient-Actor-Critic knightron0/cartpole-openAI cbalkig/DL-RL-Gamer mtimjones/QLearning reevesba/adversarial-ml Khrylx/PyTorch-RL roboserg/RoboLeague 全部项目