Static routing

About: Static routing is a research topic. Over the lifetime, 25733 publications have been published within this topic receiving 576732 citations.

Router and routing protocol redundancy

Abstract: A router and routing protocol redundancy are disclosed to reduce service outage or degradation for a network device and thus to increase service availability on a network due to software and hardware failures of the network device. A network device such as router includes a redundancy platform having an active controller system and a standby controller system. A routing protocol state change is received or generated by the active controller system. The received or generated routing protocol state change is replicated to the standby controller system. By replicating the routing protocol state change, the standby controller system can maintain the routing protocol sessions for the network device if a failure occurs in the active controller system. Furthermore, the routing protocol states are maintained in realtime to handle the dynamic changes created by routing protocols.

Embedded routing algorithms under the internet protocol routing layer of a software architecture protocol stack in a mobile ad-hoc network

Abstract: A novel software architecture protocol stack with embedded routing algorithms under the Internet Protocol (IP) routing layer in order to provide high quality distribution of multimedia (voice, video, and data) services. These routing algorithms may include Logical Link Control, Adaptive Transmission Protocol, Neighbor Discovery, Traffic Control, Ad-Hoc Routing, Flow Processing, Intelligent Access and Admission Control. These routing algorithms when implemented further provide for advantages in speed of service, reliability of service, self-healing in case of catastrophic failure of an infrastructure component, load-balancing and geographic reuse.

On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

Abstract: Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.

A hierarchical clustering and routing procedure for large scale disaster relief logistics planning

Abstract: We describe a hierarchical cluster and route procedure (HOGCR) for coordinating vehicle routing in large-scale post-disaster distribution and evacuation activities. The HOGCR is a multi-level clustering algorithm that groups demand nodes into smaller clusters at each planning level, enabling the optimal solution of cluster routing problems. The routing problems are represented as capacitated network flow models that are solved optimally and independently by CPLEX on a parallel computing platform. The HOGCR preserves the consistency among parent and child cluster solutions obtained at consecutive levels. We assess the performance of the algorithm by using large scale scenarios and find satisfactory results.