Nines are not enough: meaningful metrics for clouds | the morning paper
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic Nines are not enough: meaningful metrics for clouds Mogul & Wilkes, HotOS’19 It’s hard to define good SLOs, especially when outcomes aren’t fully under the control of any single party. The authors of today’s paper should know a thing or two about that: Jeffrey Mogul and John Wilke
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification
Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification Zhiwu Huang†‡ ZHIWU.HUANG@VIPL.ICT.AC.CN Ruiping Wang†§ WANGRUIPING@ICT.AC.CN Shiguang Shan†§ SGSHAN@ICT.AC.CN Xianqiu Li†‡ XIANQIU.LI@VIPL.ICT.AC.CN Xilin Chen†§ XLCHEN@ICT.AC.CN † Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of
Data-Driven Metric Development for Online Controlled Experiments: Seven Lessons Learned
Data-Driven Metric Development for Online Controlled Experiments: Seven Lessons Learned Online controlled experiments, also called A/B testing, have been established as the mantra for data-driven decision making in many web-facing companies. In recent years, there are emerging research works focusing on building the platform and scaling it up [34], best practices and lessons learned to obtain trus
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
https://dl.acm.org/doi/10.1145/3041021.3054199
Measuring the User Experience on a Large Scale: User-Centered Metrics for Web Applications
Rethinking Metrics: Metrics 2.0 | USENIX
