Tata Consultancy Services Limited

Subhasri Duttagupta

Manoj Karunakaran Nambiar

Mukund Kumar

Dheeraj Chahal

Dhaval Shah

Rupinder Singh Virk

Nambiar Manoj Karunakaran

Manoj Karuna Karan Nambiar

Performance prediction systems and method of an Internet of Things (IoT) platform and applications includes obtaining input(s) comprising one of (i) user requests and (ii) sensor observations from sensor(s); invoking Application Programming Interface (APIs) of the platform based on input(s); identifying open flow (OF) and closed flow (CF) requests of system(s) connected to the platform; identifying workload characteristics of the OF and CF requests to obtain segregated OF and segregated CF requests, and a combination of open and closed flow requests; executing performance tests with the APIs based on the workload characteristics; measuring resource utilization of the system(s) and computing service demands of resource(s) from measured utilization, and user requests processed by the platform per unit time; executing the performance tests with the invoked APIs based on volume of workload characteristics pertaining to the application(s); and predicting, using queuing network, performance of the application(s) for the volume of workload characteristics.

Systems and methods for predicting performance of applications on an internet of things (IoT) platform

Systems and methods for benchmark based cross platform service demand prediction includes generation of performance mimicking benchmarks that require only application level profiling and provide a representative value of service demand of an application under consideration on a production platform, thereby eliminating need for actually deploying the application under consideration on a production platform. The PMBs require only a representative estimate of service demand of the application under test and can be reused to represent multiple applications. The PMBs are generated based on a skeletal benchmark corresponding to the technology stack used by the application under test and an input file generated based on application profiling that provides pre-defined lower level method calls, data flow sequences between multi-tiers of the application under test and send and receive network calls made by the application under consideration.

Systems and methods for benchmark based cross platform service demand prediction

System and method for predicting performance of a software application over a target system is disclosed. The method comprises generating a benchmark suite such that benchmark indicates a combination of workloads applied over a set of standard software applications running on a source system. The method further comprises identifying a benchmark of the benchmark suite, wherein the benchmark has performance characteristics same as that of the software application. The method further enables remotely executing the set of standard software applications associated with the benchmark on the target system with the combination of workload as specified by the benchmark. The method further enables recording a performance of the set of standard software applications on the target system. Based on the performance of the standard software applications on the target system the performance of the software application is predicted.

Predicting performance of a software application over a target system

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.

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Mumbai, India

Subhasri Duttagupta