Poster: online adaptive sampling for network delay measurement via matrix completion

End-to-end network delay plays an important role in distributed services. Unfortunately, it is infeasible to know all node-pair delay information in practice due to the quadratic growth of overhead by active probing. In this paper, we leverage the state-of-the-art matrix completion technology for better network delay estimation from limited measurements. Specifically, we formulate the matrix completion problem as a nuclear norm minimization problem which can be solved via convex optimization. We propose an online adaptive sampling strategy for network delay measurement. The key idea is to sample the elements with larger leverage scores to maintain characteristic of important rows or columns of the matrix. The number of samples is adaptively determined by a proposed stopping criterion. A preliminary simulation result based on real-world network delay datasets indicates that our proposed sampling algorithm is capable of providing better performance (smaller estimation error and less convergence stress) at less cost (fewer samples and shorter processing time) than other traditionally used algorithms.