Tomogravity space based traffic matrix estimation in data center networks

Abstract

Traffic matrix (TM) is an important input requirement to better system management in data center networks (DCNs). Directly estimating TM is cost and difficult since the flow behaviors in DCNs are irregular and the TM across the Top of Rack (ToR) switches is huge. Although indirect TM estimation tomography based methods can be applied in DCNs after decomposing tree-like structure, these approaches require a good prior TM obtained by gravity model to improve estimation accuracy. In addition, data collection from the Simple Network Management Protocol (SNMP) employed in DCNs can result in unavoidable data missing and data errors. Therefore, it is necessary to estimate a good prior TM and study the effect of link data missing or data errors on estimation accuracy. In this paper, we utilize the tomogravity space to achieve TM estimation in decomposed tree-like DCNs without requiring a good prior TM because the gravity model can be replaced by gravity space. We propose two iterative algorithms to estimate TM between tomogravity space and gravity space, and use similar-Mahalanobis distance as a metric to control estimation errors. One iterative algorithm utilizes a prior TM calculated based on coarse-grained traffic characteristics, whereas the other considers moderate link data missing and no prior TM based on traffic characteristics. To further separately discuss the effect of link data errors, we obtain desirable link measurement from packet trace and routing matrix. Numerical results demonstrate that our iterative algorithms outperform the existing algorithms in terms of controlling data errors based on decomposed structure and produce robust results when adding different noise level on the link data.