Locality Matters! Traffic Demand Modeling in Datacenter Networks
Zhiwen Liu, Mowei Wang, Yong Cui · 2022
Understanding and modeling traffic demand characteristics in datacenter networks is of great importance for datacenter network optimization. However, prior traffic models are over-simplified and insufficient in capturing the complex locality properties of traffic demand. We analyze real-world traffic traces and discover strong dependency between the spatial attributes (source, destination) and non-spatial attributes (interarrival time, flow size) of traffic demand. We propose Lomas to model the joint distribution of multi-dimensional traffic demand attributes and generate synthetic traces. Lomas is a novel extension of hierarchical Bayes model that can represent the relationships among these attributes as a dependency graph. We validate Lomas by showing its ability to recreate the flow-level traffic demand patterns of real-world traffic traces. Our approach can be easily adapted to different datacenters with heterogeneous traffic demand patterns, making it a convenient tool for practitioners to utilize.