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近日,全球分析师大会HAS 2020期间,华为面向全球发布《自动驾驶网络解决方案白皮书》,系统阐述未来网络架构、运维架构和其关键技术,通过网元、网络和云端的三层AI能力协同,使能网络走向极简超宽、运维迈向人机协同,为运营商和产业伙伴的数字化转型提供实践参考。

华为自动驾驶网络ADN目标架构

  华为公共开发部总裁鲁鸿驹表示:“未来十年是智能时代蓬勃发展的黄金十年,以5G、云、AI为核心代表的新技术将赋予联接智能升级的核心动能。华为呼吁业界同仁一同探索实践,通过数据与知识驱动,打造一张自动、自愈、自优的自治网络,抓住数字经济所赋予的新机遇。“

  白皮书指出,打造自动驾驶网络需做出两大转变:

  第一,从“以网元为中心”的碎片化建网模式,转变为“以业务为中心”的积木式的自治域建网模式。通过融合的“管理-控制-分析” 实现单域自治和实时闭环,平衡域内创新和域间协同的成本与速度;

  第二,产业携手定义跨域开放协同的目标架构和可编程的API标准,大幅简化跨域业务协同和保障的复杂性,降低研运成本和风险,简化集成敏捷商业,降低整个产业的协作成本。

  同时,白皮书建议以L4级(高度自动驾驶网络)作为未来架构的阶段性目标,应该具备以下四个特征:一、网络知识和专家知识数字化,从被动的人工运维走向预测性的智能运维;二、极简架构的网络基础设施,网元走向智能化;三、分层的单域自治和跨域协同,网络走向在线实时闭环;四、统一的云端AI训练、知识管理和运维设计平台,支持电信网络迭代演进。

  白皮书呼吁业界要实现自动驾驶网络的宏伟目标,需要产业各方达成共识,按照开发一代、研究一代、探索一代的方式共同制定统一标准和分级评估体系,形成高效协同的产业生态,共同助力产业智能升级和健康可持续性发展。

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Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs. Elastic training is a service embedded in cloud training platforms that dynamically scales up or down the resources allocated to a job. The core technique of an elastic training system is to best allocate limited resources among heterogeneous jobs in terms of shorter queueing delay and higher training efficiency. This paper presents an optimal resource allocator for elastic training system that leverages a mixed-integer programming (MIP) model to maximize the training progress of deep learning jobs. We take advantage of the real-world job data obtained from ModelArts, the deep learning training platform of Huawei Cloud and conduct simulation experiments to compare the optimal resource allocator with a greedy one as benchmark. Numerical results show that the proposed allocator can reduce queuing time by up to 32% and accelerate training efficiency by up to 24% relative to the greedy resource allocator, thereby greatly improving user experience with Huawei ModelArts and potentially enabling the realization of higher profits for the product. Also, the optimal resource allocator is fast in decision-making, taking merely 0.4 seconds on average.

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