Flux Research Group / School of Computing

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

Hyunwook 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.