# Modeling and Routing for Predictable Dynamic Networks: This paper is only for copyright protection, and unpublished to the top-level version

## TL;DR

This paper introduces a topology modeling and routing scheme for predictable dynamic networks, significantly reducing route changes and improving stability and performance in space Internet scenarios.

## Contribution

It presents a Divide-and-Merge topology model and a dynamic programming algorithm for routing updates, along with a stable routing scheme incorporating link lifetime metrics.

## Key findings

- Reduces number of route changes and affected nodes
- Improves network stability and throughput
- Lowers packet loss and delay variation

## Abstract

The topologies of predictable dynamic networks are continuously dynamic in terms of node position, network connectivity and link metric. However, their dynamics are almost predictable compared with the ad-hoc network. The existing routing protocols specific to static or ad-hoc network do not consider this predictability and thus are not very efficient for some cases. We present a topology model based on Divide-and-Merge methodology to formulate the dynamic topology into the series of static topologies, which can reflect the topology dynamics correctly with the least number of static topologies. Then we design a dynamic programing algorithm to solve that model and determine the timing of routing update and the topology to be used. Besides, for the classic predictable dynamic network---space Internet, the links at some region have shorter delay, which leads to most traffic converge at these links. Meanwhile, the connectivity and metric of these links continuously vary, which results in a great end-to-end path variations and routing updates. In this paper, we propose a stable routing scheme which adds link life-time into its metric to eliminate these dynamics. And then we take use of the Dijkstra's greedy feature to release some paths from the dynamic link, achieving the goal of routing stability. Experimental results show that our method can significantly decrease the number of changed paths and affected network nodes, and then greatly improve the network stability. Interestingly, our method can also achieve better network performance, including the less number of loss packets, smoother variation of end-to-end delay and higher throughput.