Adaptive traffic management for high-capacity network architectures with smart services

网络服务正在从基于客户端-服务器的服务演变为位置和上下文感知的分布式服务。未来的智能服务将使用上下文和位置将信息动态推送到用户设备。随着这些智能网络服务的快速增长，调整网络技术以使其能够在安全的可扩展平台上提供服务敏捷性非常重要。未来互联网的用户将需要一个安全、节能、可自我管理和可配置的网络平台。我们的研究计划的目标是在网络流量管理层面上研究有效支持智能服务的方法和技术。最有前景的研究方法之一是使用自主通信来实现网络和设备的自我配置、自我供应和自我管理。我们将通过监控网络资源来设计动态资源管理的方法和算法。随着链路速度的不断提高，如何设计能够应对流媒体音频和视频流的高数据速率的路由器和交换设备是一个具有挑战性的问题。我们研究这一问题的方法是研究可扩展的基于分组的路由/交换体系结构，以有效地传输如此大容量的数据。如今的网络大多通过专用终端系统检测垃圾邮件、病毒和其他恶意网络使用。高速主干路由器通常不会直接使用太多智能来防御这些。我们的研究小组将开发方法和技术，将传输中的无状态设计整合到主干路由器中，以检测恶意网络使用情况，作为网络自卫机制。因此，我们的研究将为未来大容量网络体系结构的关键领域贡献高效和安全的方法、算法和技术。这项研究是及时的，对我们国家具有至关重要的战略意义。网络的自主性越强，系统越简单，恢复能力越强，稳定性越高，可用性越高，错误越少，安全性也越高。研究网络流量管理层面的方法和技术对于智能网络服务的未来至关重要。

Network services are evolving from client-server based services to location and context-aware distributed services. Future smart services will use context and location to push information to user devices dynamically. With the rapid growth of these smart network services, it is important to adapt the network technology so that it can provide service agility over a secure scalable platform. Users of the future Internet will demand a secure, energy-efficient, self-manageable, and configurable network platform. The objective of our research program is to investigate methods and techniques at the network traffic management level to support smart services effectively. One of the most promising research approaches is to employ autonomic communications for network and device self-configuration, self-provisioning, and self-management. We will design dynamic resource management methods and algorithms by monitoring network resources. With ever increasing link speeds, it is a challenging problem to design routers and switching equipment that can cope with the high data rates of streaming audio and video. Our approach to this research problem is to investigate scalable packet based routing/switching architectures to transport such high volumes data effectively. Today's networks mostly detect spam, viruses, and other malicious network usage through dedicated end systems. High-speed backbone routers generally do not directly employ much intelligence to defend these. Our research group will develop methods and techniques to incorporate in-transit stateless designs in the backbone routers to detect malicious network usage as a network self-defence mechanism. Thus, our research will contribute highly efficient and secure methods, algorithms, and techniques to the critical area of future high capacity network architectures. This research is timely and of critical strategic importance for our nation. More autonomicity in networks will lead to simpler systems, better resiliency, more stability, higher availability, fewer errors, and higher security. Investigating methods and techniques at the network traffic management level is critical for the future of smart network service.