公开(公告)号：US20190140910A1

公开(公告)日：2019-05-09

申请号：US15803501

申请日：2017-11-03

Applicant: salesforce.com, inc.

Inventor： Tejaswini Ganapathi ,  Satish Raghunath ,  Xu Che ,  Shauli Gal ,  Andrey Karapetov

IPC: H04L12/24 ,  G06F17/16 ,  G05B17/02 ,  H04L12/26 ,  G06N7/00 ,  G06F17/30

Abstract: An data driven approach to generating synthetic data matrices is presented. By retrieving historical network traffic data, probabilistic models are generated. Optimal distribution families for a set of independent data segments are determined. Applications are tested and performance metrics are determined based on the generated synthetic data matrices.

公开(公告)号：US10205634B2

公开(公告)日：2019-02-12

申请号：US15593635

申请日：2017-05-12

Applicant: salesforce.com, inc.

Inventor： Shauli Gal ,  Satish Raghunath ,  Kartikeya Chandrayana ,  Tejaswini Ganapathi

IPC: H04L12/26 ,  H04L12/24 ,  H04L29/06 ,  H04L29/08

Abstract: An adaptive multi-phase approach to estimating network parameters is presented. By gathering and aggregating raw network traffic data and comparing against default network parameters, a training data set may be generated. A black box optimization may be used in tandem with a supervised learning algorithm to bias towards better choices and eventually pick network parameters which optimize performance. Data delivery strategies are applied to deliver content using the optimized network policies based on the estimated parameters.