公开(公告)号：US20210390033A1

公开(公告)日：2021-12-16

申请号：US17345166

申请日：2021-06-11

Applicant: Tata Consultancy Services Limited

Inventor： Rekha SINGHAL ,  Gautam SHROFF ,  Dheeraj CHAHAL ,  Mayank MISHRA ,  Shruti KUNDE ,  Manoj NAMBIAR

IPC: G06F11/36 ,  G06F9/48 ,  G06N3/02 ,  G06K9/62

Abstract: This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

公开(公告)号：US20170153963A1

公开(公告)日：2017-06-01

申请号：US15361129

申请日：2016-11-25

Applicant: TATA CONSULTANCY SERVICES LIMITED

Inventor： Dheeraj CHAHAL ,  Rupinder Singh VIRK ,  Manoj Karunakaran NAMBIAR

IPC: G06F11/34 ,  G06F11/30 ,  G06F17/30 ,  G06F11/36

Abstract: 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.

公开(公告)号：US20230409967A1

公开(公告)日：2023-12-21

申请号：US18140219

申请日：2023-04-27

Applicant: Tata Consultancy Services Limited

Inventor： Dheeraj CHAHAL ,  Surya Chaitanya Venkata PALEPU ,  Mayank MISHRA ,  Ravi Kumar SINGH ,  Rekha SINGHAL

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: State of the art methods require size of DL model, or its gradients be less than maximum data item size of storage used as a communication channel for model training with serverless platform. Embodiments of the present disclosure provide method and system for training large DL models via serverless architecture using communication channel when the gradients are larger than maximum size of one data item allowed by the channel. Gradients that are generated by each worker during current training instance, are chunked into segments and stored in the communication channel. Corresponding segments of each worker are aggregated by aggregators and stored back. Each of the aggregated corresponding segments are read by each worker to generate an aggregated model to be used during successive training instance. Optimization techniques are used for reading-from and writing-to the channel resulting in significant improvement in performance and cost of training.

公开(公告)号：US20180217913A1

公开(公告)日：2018-08-02

申请号：US15882568

申请日：2018-01-29

Applicant: Tata Consultancy Services Limited

Inventor： Dheeraj CHAHAL ,  Manoj Karunakaran NAMBIAR

IPC: G06F11/34 ,  G06F11/30

CPC classification number: G06F11/3457 ,  G06F11/302 ,  G06F11/3041 ,  G06F11/3414 ,  G06F11/3428 ,  G06F11/349 ,  G06F11/3495

Abstract: The present disclosure provides systems and methods for performance evaluation of Input/Output (I/O) intensive enterprise applications. Representative workloads may be generated for enterprise applications using synthetic benchmarks that can be used across multiple platforms with different storage systems. I/O traces are captured for an application of interest at low concurrencies and features that affect performance significantly are extracted, fed to a synthetic benchmark and replayed on a target system thereby accurately creating the same behavior of the application. Statistical methods are used to extrapolate the extract features to predict performance at higher concurrency level without generating traces at those concurrency levels. The method does not require deploying the application or database on the target system since performance of system is dependent on access patterns instead of actual data. Identical access patterns are re-created using only replica of database files of the same size as in the real database.