Microsoft Technology Licensing, LLC

T-mobile Usa, Inc.

Z Morley Mao

Paramvir Bahl

Ming Zhang

Jie Hui

Antoine T Tran

Ying Zhang

Kranthi Sontineni

Yunhan Jia

Yihua Guo

Alex Yoon

A VoLTE diagnostic engine may receive VoLTE call records of VoLTE calls that are carried by a wireless carrier network for multiple user devices. The VoLTE call records may include performance indicators and call features for the voice calls. Each call feature of a VoLTE call may represent a circumstance under which the VoLTE call is established and ended. The VoLTE diagnostic engine may apply a decision tree to the VoLTE call records to identify critical features of one or more call conditions that lead to Quality of Experience (QoE) problems for the VoLTE calls captured in the VoLTE call records. Each call condition may include a subset of the call features. Further, the VoLTE diagnostic engine may apply a classifier on the critical features to determine a root cause of a QoE problem for at least one call conditions.

Machine learning-based troubleshooting of VoLTE calls

Techniques and systems for providing capacity planning for data center services are disclosed herein. In some aspects, a multi-tier service is analyzed to determine a query response time (QRT) that can be used for resource planning or other service management reasons. The QRT may include a front end (FE) response time of FE servers and a back end (BE) response time of BE servers, where the BE servers are partitioned servers, and where the response times are based on various request rates from users. In various aspects, the QRT may include a network transmission time of data transmitted between an end user and each tier. In further aspects, the FE response time may be modeled using a single server queuing model while the BE response time may be modeled using a simulation or regression analysis.

Capacity planning for data center services

Growing community of inventors

Ann Arbor, MI, United States of America

Z Morley Mao