Tata Consultancy Services Limited

Rupinder Singh Virk

Manoj Karunakaran Nambiar

Gautam Shroff

Lovekesh Vig

Rekha Singhal

Dheeraj Chahal

Subhasri Duttagupta

Sanket Kadarkar

Siddharth Verma

Sharod Roy Choudhury

Vartika Tewari

Manoj Karuna Karan Nambiar

Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

Systems and methods for real time configurable recommendation using user data

A method and system is provided for pre-deployment performance estimation of input-output intensive workloads. Particularly, the present application provides a method and system for predicting the performance of input-output intensive distributed enterprise application on multiple storage devices without deploying the application and the complete database in the target environment. The present method comprises of generating the input-output traces of an application on a source system with varying concurrencies; replaying the generated traces from the source system on a target system where application needs to be migrated; gathering performance data in the form of resource utilization, through-put and response time from the target system; extrapolating the data gathered from the target system in order to accurately predict the performance of multi-threaded input-output intensive applications in the target system for higher concurrencies.

Method and system for pre-deployment performance estimation of input-output intensive workloads

The present disclosure generally relates to a system and method for predicting performance of a multi-threaded application, and particularly, to a system and method for predicting performance of the multi-threaded application in the presence of resource bottlenecks. In one embodiment, a system for predicting performance of a multi-threaded software application is disclosed. The system may include one or more processors and a memory storing processor-executable instructions for configuring a processor to: represent one or more queuing networks corresponding to resources, the resources being employed to run the multi-threaded application; detect, based on the one or more queuing networks, a concurrency level associated with encountering of a first resource bottleneck; determine, based on the concurrency level, performance metrics associated with the multi-threaded application; and predict the performance of the multi-threaded application based on the performance metrics.

System and method facilitating performance prediction of multi-threaded application in presence of resource bottlenecks

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Mumbai, India

Rupinder Singh Virk