实时世界中的敏捷性和脆弱性

Gilles Grapinet
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引用次数: 0

摘要

联网移动设备现在已经到了被视为一种商品的时代。用户希望无论在旅途中还是在家中都能享受到相同水平的服务和性能。消费电子产品(如智能手表、NEST传感器和自动驾驶汽车)中机器学习的整合带来的新一波云支持服务正在以颠覆性的力量进入市场。万物互联已经出现在我们的手腕、汽车、建筑和口袋中,并正在推动新的经济模式(如共享经济或数据经济)。当前的技术正在努力满足新应用和服务带来的需求;具体来说,它缺乏适应快速变化的业务模型和技术需求所需的敏捷性;它也带来了脆弱性,因为我们目前的大多数基础设施、系统和解决方案都没有设计成允许在个人层面上大量使用实时应用程序。安全性、连接性、移动性、延迟和可伸缩性只是为实现无缝用户体验铺平道路的众多挑战中的一部分。设备对设备通信以及新的商业和信任模式将主导互联网。万物互联的成功至关重要,行业、政府和学术界联手构建全新的协议架构、计算范式和监管框架,旨在支持移动性、高性能服务、强大的安全性和精细的用户隐私粒度,以适应不断扩大的个人数据传播水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agility and Fragility in a Real-time World
Connected mobile devices have now reached the age of being perceived as a commodity. Users expect to be able to enjoy the same level of service and performance both on the go and at home. A new wave of cloud-supported services brought about by the integration of machine learning in consumer electronics –such as Smart watches, NEST sensors, and Self-Driving cars –is entering the market with a disruptive power. The Internet of Everything is already on our wrists, cars, buildings, and pockets, and is pushing through new economic models (such as Shared Economy or Data Economy). Current technology is struggling to meet the demands posed by new applications and services; specifically, it lacks the agility needed to adapt to fast-changing business models and technical requirements; it also comes with fragilities as most of our current infrastructures, systems and solutions were not designed to allow massive usage of real-time applications at personal level. Security, connectivity, mobility, latency, and scalability are just some of the many challenges that pave the way towards a seamless user experience. Device-todevice communications along with new business and trust models will dominate the Internet. It is essential for the Internet of Everything to succeed, and that industry, government and academia join forces to build brand new protocol architectures, computing paradigms, and regulatory frameworks designed to support mobility, high performance services, strong security and a fine user privacy granularity adapted to an expanding level of personal data dissemination.
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