Flux Research Group / School of Computing

Polygravity: Traffic Usage Accountability via Coarse-grained Measurements in Multi-tenant Data Centers

Hyun-wook Baek, Cheng Jin, Guofei Jiang, Cristian Lumezanu, Jacobus (Kobus) Van der Merwe, Ning Xia, and Qiang Xu

Proceedings of the 8th ACM Symposium on Cloud Computing (SoCC) 2017.

DOI: 10.1145/3127479.3129258



Network usage accountability is critical in helping operators and customers of multi-tenant data centers deal with concerns such as capacity planning, resource allocation, hotspot detection, link failure detection, and troubleshooting. However, the cost of measurements and instrumentation to achieve flow-level accountability is non-trivial. We propose Polygravity to determine tenant traffic usage via lightweight measurements in multi-tenant data centers. We adopt a tomogravity model widely used in ISP networks, and adapt it to a multi-tenant data center environment. By integrating datacenter-specific domain knowledge, sampling-based partial estimation and gravity-based internal sinks/sources estimation, Polygravity addresses two key challenges for adapting tomogravity to a data center environment: sparse traffic matrices and internal traffic sinks/sources. We conducted extensive evaluation of our approach using realistic data center workloads. Our results show that Polygravity can determine tenant IP flow usage with less than 1% average relative error for tenants with fine-grained domain knowledge. In addition, for tenants with coarse-grained domain knowledge and with partial host-based sampling, Polygravity reduces the relative error of sampling-based estimation by 1/3.