.png){kind=icon}
.png){kind=icon}
Return
Improving Centralized Path Calculation Based on Graph Compression
.png){kind=icon}
PRE
AI
DOI：10.1109/CC.2018.8398509.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Shortest-path calculation on weighted graphs are an essential operation in computer networks. The performance of such algorithms has become a critical challenge in emerging software-defined networks (SDN), since SDN controllers need to centralizedly perform a shortest-path query for every flow, usually on large-scale network. Unfortunately, one of the challenges is that current algorithms will become incalculable as the network size increases. Therefore, inspired by the compression graph in the field of compute visualization, we propose an efficient shortest path algorithm by compressing the original big network graph into a small one, but the important graph properties used to calculate path is reserved. We implement a centralized version of our approach in SDN-enabled network, and the evaluations validate the improvement compared with the well-known algorithms.
Keywords:
graph representation
path compression
shortest path
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
1.9K
Citations:
5.0K