Name
Abstract | ||
|---|---|---|
Online cloud services need to fulfill clients’ requests scalably and fast. State-of-the-art cloud services are increasingly deployed as a distributed service mesh. Service to service communication is frequent in the mesh. Unfortunately, problematic events may occur between any pair of nodes in the mesh, therefore, it is vital to maximize the network visibility. A state-of-the-art approach is to model pairwise RTTs based on a latent factor model represented as a low-rank matrix factorization. A latent factor corresponds to a rank-1 component in the factorization model, and is shared by all node pairs. However, different node pairs usually experience a skewed set of hidden factors, which should be fully considered in the model. In this paper, we propose a skewness-aware matrix factorization method named SMF. We decompose the matrix factorization into basic units of rank-one latent factors, and progressively combine rank-one factors for different node pairs. We present a unifying framework to automatically and adaptively select the rank-one factors for each node pair, which not only preserves the low rankness of the matrix model, but also adapts to skewed network latency distributions. Over real-world RTT data sets, SMF significantly improves the relative error by a factor of 0.2
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to 10
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, converges fast and stably, and compactly captures fine-grained local and global network latency structures. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TNET.2019.2923815 | IEEE/ACM Transactions on Networking |
Keywords | Field | DocType |
Matrix decomposition,Adaptation models,Measurement,Predictive models,Servers,Routing,Interpolation | Pairwise comparison,Data set,Latency (engineering),Computer science,Matrix decomposition,Server,Theoretical computer science,Factorization,Approximation error,Cloud computing,Distributed computing | Journal |
Volume | Issue | ISSN |
27 | 4 | 1063-6692 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongquan Fu | 1 | 36 | 11.32 |
| Dongsheng Li | 2 | 299 | 60.22 |
| Pere Barlet-ros | 3 | 269 | 27.74 |
| Chun Huang | 4 | 13 | 8.00 |
| Zhen Huang | 5 | 57 | 20.78 |
| Siqi Shen | 6 | 135 | 14.47 |
| Huayou Su | 7 | 52 | 11.84 |