Abstract: Heterogeneous cloud storage services offered by different cloud service providers have unique deliverable performance. One key challenge is to find the maximum achievable data transfer rate from one cloud service to another. The disclosure herein generally relates to cloud computing, and, more particularly, to a method and system for parameter tuning in cloud network. The system obtains optimum value of parameters of a source cloud and a destination cloud in a cloud pair, by performing a parameter tuning. The optimum value of parameters and corresponding data transfer rate is used as a training data to generate a data model. The data model processes real-time information with respect to cloud pairs, and predicts corresponding data transfer rate.

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.

Abstract: Migrating application from on premise HPC cluster to serverless platform is tedious task and involves significant amount of human efforts as cloud infrastructure needs to be created, data along with libraries and application code need to be copied from on-premise to cloud, and application need to be made compliant for execution on cloud. Present disclosure provides method and system for performing automated migration of high performance computing application to serverless platform. The system first check cloud readiness of application based on operation qualification parameters of application. In case application is found to be cloud ready, the system determines whether application can be executed on serverless platform based on execution time of the application and permissible limits defined for application in service level agreements.

Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

Abstract: Recent techniques for workload characterization of an application to be executed in a serverless execution environment or cloud are based on benchmark-approximation. Multiple microbenchmarks are run against the multiple VM configurations and a score is calculated which is used for mapping futuristic workloads to the appropriate configuration. Embodiments herein disclose method and system for workload characterization-based capacity planning of an actual application running on-premise with different configurations of the same machine and providing a cost-effective and high-performance serverless execution environment. Resource demand of each API in the application workflow is evaluated. Based on the resource demand of each API, a mapping is performed to the serverless platform on cloud. Additionally, characterization of threads within each API is performed and each thread is mapped to a serverless instance based on its resource requirements.

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.

Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

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.

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.

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.

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.

Abstract: 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.

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.

Abstract: 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.