Abstract: Data center network (DCN) is the infrastructure of cloud computing and other distributed computing ser-vices.Understanding the characteristics of end-to-end traffic flows in DCNs is essential to DCN designs and operations.However,it is extremely difficult to measure the traffic flows directly.Due to the distinct structure of DCNs,the traditional traffic estimation method can not be applied to DCNs yet.To address this problem,we first extracted the coarse-grained traffic characteristics based on the user resource allocation and link utilization.And then an efficient traffic estimation algorithm was proposed for DCNs based on the gravity traffic model and network tomography.We compared our new proposal with two classical traffic inference algorithms Tomogravity and ELIA on different scale of DCNs.The results show that new algorithm outperforms the other two algorithms in both speed and accuracy.With the new method,the network managers can obtain the end-to-end traffic on DCNs in real time.

Key words: Data center networks,Network measurement,Traffic inference,Traffic gravity model,Network tomography