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

网络服务正从基于客户端-服务器的服务发展到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来动态地向用户设备推送信息。随着这些智能网络服务的快速增长，重要的是调整网络技术，以便它可以在安全的可扩展平台上提供服务敏捷性。未来互联网的用户将需要一个安全、节能、自我管理和可配置的网络平台。我们的研究计划的目标是调查的方法和技术在网络流量管理水平，以有效地支持智能服务。最有前途的研究方法之一是采用自主通信的网络和设备的自我配置，自我配置和自我管理。通过对网络资源的监控，设计动态资源管理方法和算法。随着链路速度的不断提高，设计能够科普流音频和视频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。我们的方法来解决这个研究问题是调查可扩展的基于分组的路由/交换架构，有效地传输这样的大容量数据。当今的网络大多通过专用的终端系统检测垃圾邮件、病毒和其他恶意网络使用。高速骨干路由器通常不会直接使用太多的智能来保护它们。我们的研究小组将开发方法和技术，在骨干路由器中加入在途无状态设计，以检测恶意网络使用，作为网络自卫机制。因此，我们的研究将有助于高效和安全的方法，算法和技术的关键领域，未来的高容量网络架构。这项研究是及时的，对我们国家具有至关重要的战略意义。网络中更高的灵活性将导致更简单的系统，更好的弹性，更稳定，更高的可用性，更少的错误和更高的安全性。研究网络流量管理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.