Topology calibration in data centers

Abstract

The topology of data centers changes dynamically due to link malpositions, hardware failures or software crushes. However, many topology enabled protocols or applications must know the current topology of data center precisely, which triggers the topology calibration problem. Topology calibration needs to deduce the different nodes and links between two given topologies effectively. Based on the existing method, deriving the different nodes is relatively simple, since they can be uniquely identified by their IP or MAC addresses. On the contrary, picking the different links from the massive links can be costly. Therefore, we envision a method to locate the different links with respect to the following rationales: 1) efficient, the caused storage cost or communication overhead should be low; 2) without priori knowledge, there is no support information, thus the different links should be decoded inversely. However, the existing strategies based on Bloom filter, Hash table, or Search trees fail to achieve the two rationales simultaneously. Thus, we propose graph filter, a space-efficient data structure to represent and deduce the different links in an invertible manner. To this end, the associated encoding, subtracting and decoding algorithms are proposed. The simulations highlight the strength of graph filter reasonably.