ATME: Accurate Traffic Matrix Estimation in Both Public and Private Datacenter Networks

Understanding the pattern of end-to-end traffic flows in
datacenter networks
(DCNs) is essential to many DCN designs and operations (e.g., traffic engineering and load balancing). However, little research work has been done to obtain traffic information efficiently and yet accurately. Researchers often assume the availability of traffic tracing tools (e.g., OpenFlow) when their proposals require traffic information as input, but these tools may have high monitoring overhead and consume significant switch resources even if they are available in a DCN. Although estimating the
traffic matrix
(TM) between origin-destination pairs using only basic switch SNMP counters is a mature practice in IP networks, traffic flows in DCNs show totally different characteristics, while the large number of redundant routes in a DCN further complicates the situation. To this end, we propose to utilize resource provisioning information in public cloud datacenters and the service placement information in private datacenters for deducing the correlations among top-of-rack switches, and to leverage the uneven traffic distribution in DCNs for reducing the number of routes potentially used by a flow. These allow us to develop ATME as an efficient TM estimation scheme that achieves high accuracy for both public and private DCNs. We compare our two algorithms with two existing representative methods through both experiments and simulations; the results strongly confirm the promising performance of our algorithms.