EVENT OPTIMIZATION IN A MULTI-TENANT COMPUTING ENVIRONMENT

Machine learning-based techniques are described that enable modifying an event timing schedule in a multi-tenant computing environment. The multi-tenant computing environment stores tenant data of multiple tenants. Each tenant of the multiple tenants offers subscription services to subscribers. Mult...

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Bibliographic Details
Main Authors Chiu, Anthony, Shaik, Amanulla, Sudersanam, Sumithra, Mukhopadhyay, Dibya, Sooklaris, Maria Nicoletta, Krishnaswamy, Arun, Pitchumani, Vinodhini
Format Patent
LanguageEnglish
Published 19.09.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Machine learning-based techniques are described that enable modifying an event timing schedule in a multi-tenant computing environment. The multi-tenant computing environment stores tenant data of multiple tenants. Each tenant of the multiple tenants offers subscription services to subscribers. Multiple events involving multiple subscribers of a particular tenant are attempted. The multiple events include a first subset of successfully executed events and a second subset of unsuccessfully executed events. One or more training datasets are generated based on the first subset and the second subset. The one or more training datasets include contextual information corresponding to each of the multiple events. The contextual information includes multilevel data. A machine learning model is trained to output a timing schedule for retrying a particular unsuccessfully executed event of a particular subscriber.
Bibliography:Application Number: US202418651546