KCN: Knowledge Centric Networking

KCN：以知识为中心的网络

• 批准号: EP/L026120/1
• 负责人: George Pavlou
• 金额: $ 125.24万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL026120%2F1
• 关键词: KCN; Knowledge; Centric; Networking

The recent advent of killer applications such as content distribution, cloud computing and Internet of things (IoT), all require for the underlying network to be able to understand specific service contexts. In this project we propose the Knowledge Centric Networking (KCN) paradigm, in which knowledge is positioned at the centre of the networking landscape. The objective is to enable in-network knowledge generation and distribution in order to develop necessary network control intelligence for handling complexity and uncertainty. In order to achieve this, specific algorithms and mechanisms/protocols will be developed for knowledge acquisition, processing, dissemination and organisation both within single and across homogeneous/heterogeneous administrative domains in the Internet.The project will investigate three styles of knowledge exchange based on Software Defined Networking (SDN) principles: Knowledge as a Tool (KaaT), Knowledge as a Service (KaaS) and Knowledge as a Cloud (KaaC). KaaT will enable intelligent network operations in dynamic network environments driven by knowledge gathered at different vantage points. We advocate a hierarchical knowledge framework in which knowledge and control functions are distributed at the right places within the network for fulfilling specific control tasks. In addition, we will invetigate knowledge sharing between different players in the Internet marketplace. This can be achieved either through explicit knowledge transfer from a knowledge provider to a knowledge consumer (KaaS), or based on open knowledge clouds where knowledge prosumers may publish or subscribe to information through an open but controlled knowledge ecosystem (KaaS).The proposed KCN architecture will be validated through two complementary use cases. KCN-driven content traffic offloading between heterogeneous radio access technologies for the future mobile Internet aims to achieve adaptive resource control by taking into account a wide variety of knowledge associated with content, users and network conditions. In addition, KCN-driven energy management targets cross-layer energy saving techniques at both the IP and the physical optical layer according to the derived knowledge and dynamically changing context information.The project provides direct contributions to the TI3 sub-challenges 1, 2, 3 and 4. First of all, the KCN-based knowledge ecosystem will equip the next generation Internet with necessary intelligence for handling complex requirements under dynamic conditions. Such an ecosystem, seamlessly coupled with the SDN architecture, will be able to gracefully support the ever increasing complexity and heterogeneity of future networked services and multitude of users. The two complementary use cases demonstrate how the proposed KCN framework will be instantiated in two different application domains, content traffic offloading in mobile/wireless access networks and energy efficiency in IP/optical transport networks. Use case 1 contributes to the 3rd sub-challenge, with knowledge-based content caching and traffic offloading techniques for the future content-oriented mobile Internet. Use case 2, on the other hand, contributes to the 2nd sub-challenge with intelligent energy saving mechanisms at both the IP and optical layer. Finally, with in-network knowledge inference and learning based on raw context information, the project also addresses the 4th sub-challenge of extracting understanding from data. In summary, context information captured during network/service operation will be used to derive systematic in-network knowledge and intelligence in order to deal adaptively with both complexity and uncertainty and enable near-optimal network operation.

最近出现的杀手级应用程序，如内容分发，云计算和物联网（IoT），都需要底层网络能够理解特定的服务上下文。在这个项目中，我们提出了知识为中心的网络（KCN）的范式，其中知识被定位在网络景观的中心。我们的目标是使网络知识的生成和分发，以开发必要的网络控制智能处理复杂性和不确定性。为了实现这一目标，将开发特定的算法和机制/协议，用于在互联网上的单个和跨同质/异构管理域的知识获取、处理、传播和组织。该项目将研究基于软件定义网络（SDN）原则的三种知识交换方式：知识即工具（KaaT）、知识即服务（KaaS）和知识即云（KaaC）。KaaT将在动态网络环境中实现智能网络操作，这些环境由在不同Vantage位置收集的知识驱动。我们提倡一个层次化的知识框架，其中知识和控制功能分布在网络内的正确位置，以完成特定的控制任务。此外，我们还将研究互联网市场上不同参与者之间的知识共享。这可以通过从知识提供者到知识消费者（KaaS）的显式知识转移来实现，或者基于开放的知识云，其中知识生产者可以通过开放但受控的知识生态系统（KaaS）发布或订阅信息。未来移动的因特网的异构无线电接入技术之间的KCN驱动的内容业务卸载旨在通过考虑与内容、用户和网络条件相关联的各种知识来实现自适应资源控制。此外，KCN驱动的能源管理的目标是根据派生的知识和动态变化的上下文信息在IP和物理光层的跨层节能技术。该项目提供了直接贡献的TI 3子挑战1，2，3和4。首先，基于KCN的知识生态系统将为下一代互联网提供必要的智能，以处理动态条件下的复杂需求。这样一个与SDN架构无缝耦合的生态系统将能够优雅地支持未来网络服务和众多用户不断增加的复杂性和异构性。这两个互补的用例演示了所提出的KCN框架将如何在两个不同的应用领域中实例化，即移动的/无线接入网络中的内容流量卸载和IP/光传输网络中的能源效率。用例1对第三个子挑战做出了贡献，它为未来面向内容的移动的互联网提供了基于知识的内容缓存和流量卸载技术。另一方面，用例2通过IP层和光层的智能节能机制为第二子挑战做出了贡献。最后，通过基于原始上下文信息的网络内知识推理和学习，该项目还解决了从数据中提取理解的第四个子挑战。总之，在网络/服务操作期间捕获的上下文信息将用于获得系统的网络内知识和智能，以便自适应地处理复杂性和不确定性，并实现接近最佳的网络操作。

项目成果

QoE-Driven DASH Video Caching and Adaptation at 5G Mobile Edge

• DOI: 10.1145/2984356.2988522
• 发表时间: 2016-09
• 期刊: Proceedings of the 3rd ACM Conference on Information-Centric Networking
• 作者: Chang Ge;Ning Wang;Severin Skillman;G. Foster;Yue Cao

Towards D2D-based opportunistic data relay service in partial not-spots

基于D2D的局部非点机会数据中继服务

• DOI: 10.1109/wimob.2016.7763245
• 发表时间: 2016
• 作者: Chandrasekaran G

Dynamic link sleeping reconfigurations for green traffic engineering

• DOI: 10.1002/dac.3224
• 发表时间: 2017-06
• 期刊: International Journal of Communication Systems
• 影响因子: 2.1
• 作者: O. Okonor;Ning Wang;S. Georgoulas;Zhili Sun

Real-time QoE estimation of DASH-based mobile video applications through edge computing

• DOI: 10.1109/infcomw.2018.8406935
• 发表时间: 2018-04
• 期刊: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
• 作者: Chang Ge;Ning Wang

DR-Cache: Distributed Resilient Caching with Latency Guarantees

• DOI: 10.1109/infocom.2018.8486316
• 发表时间: 2018-04
• 期刊: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications
• 作者: Jian Li;T. K. Phan;W. Chai;D. Tuncer;G. Pavlou;D. Griffin;M. Rio