Adaptive Coherent Sampling for Network Delay Measurement

End-to-end network delay, as a metric to indicate the QoS (Quality-of-Service), plays an important role in distributed services. Unfortunately, it is infeasible in practice to know all node-pair delay information due to the quadratic growth of overhead by active probing. In this paper, we leverage the stateof-the-art matrix completion technology for better network delay estimation from limited measurements. Although the number of samples required for exact matrix completion is theoretically bounded, it is practically less helpful as the number cannot be specified. This motivates us to propose an adaptive coherent sampling algorithm to select the elements with larger leverage scores to maintain the characteristic of important rows or columns in the delay matrix. The number of samples is adaptively determined by a proposed stopping criterion. Simulation results based on real-world network delay datasets indicate that our proposed algorithm is capable of providing better performance (improves estimation error by 16.9% and convergence stress by 28.9%) at less cost (reduces number of samples by 3.9% and processing time by 78.6%) than traditionally used algorithms.