Abstract: A method and system for testing the end-to-end performance of cloud based applications. Real workload is created for the cloud based applications using synthetic users. The load and length of demand may be adjusted to address different models allowing the measurement and analysis of user performance metrics under desired conditions. Web applications and other cloud services may be tested from multiple cloud-based concurrent geographic locations in the world. The method may include generating controlled and complex web load from multiple concurrent geographic locations. The method may also include splitting traffic among multiple geographic locations. The method may also include processing test results and generating performance metrics per location as well as comparing locations. The method may also be applicable to cloud services such as mobile apps and API endpoints.

Abstract: Described herein are systems and methods of identifying and classifying performance bottlenecks for web applications. Such systems and methods use classification and analysis of performance testing data and data instrumentation via arithmetic and/or machine learning. Data is integrated from different sources including system data, historical and real time sources. Performance variations are analyzed as load changes and the impact of these variations on different sectors of the Application stack are analyzed. Bottlenecks are identified and classified based on the sector in the software stack and recommendations for optimization of an Application under Test are presented to address the bottlenecks are presented.

Abstract: A method and system for testing the end-to-end performance of cloud based applications. Real workload is created for the cloud based applications using synthetic users. The load and length of demand may be adjusted to address different traffic models allowing the measurement and analysis of user performance metrics under specified conditions.

Abstract: A method and system for testing the end-to-end performance of cloud based applications. Real workload is created for the cloud based applications using synthetic users. The load and length of demand may be adjusted to address different models allowing the measurement and analysis of user performance metrics under desired conditions. Web applications and other cloud services may be tested from multiple cloud-based concurrent geographic locations in the world. The method may include generating controlled and complex web load from multiple concurrent geographic locations. The method may also include splitting traffic among multiple geographic locations. The method may also include processing test results and generating performance metrics per location as well as comparing locations. The method may also be applicable to cloud services such as mobile apps and API endpoints.

Abstract: Described herein are systems and methods of identifying and classifying performance bottlenecks for web applications. Such systems and methods use classification and analysis of performance testing data and data instrumentation via arithmetic and/or machine learning. Data is integrated from different sources including system data, historical and real time sources. Performance variations are analyzed as load changes and the impact of these variations on different sectors of the Application stack are analyzed. Bottlenecks are identified and classified based on the sector in the software stack and recommendations for optimization of an Application under Test are presented to address the bottlenecks are presented.

Abstract: A method and system for generating performance tests for cloud based applications using data from real traffic. HTTP and API call transactions are recorded and converted to performance tests that can be used as is or manipulated for increased variability allowing for the creation of realistic performance tests for web based applications, allowing for the measurement and analysis of user performance metrics under real conditions.

Abstract: A method and system for testing the end-to-end performance of cloud based applications. Real workload is created for the cloud based applications using synthetic users. The load and length of demand may be adjusted to address different traffic models allowing the measurement and analysis of user performance metrics under specified conditions.

Abstract: An alarm management system is provided which includes a hierarchical database of alarm source identifiers. Each alarm source identifier is associated with a corresponding software entity capable of generating an alarm. The system further includes a corresponding alarm controller for each software entity and an alarm processor. The alarm processor receives an alarm from one of the software entities, accesses the hierarchical database to identify a parent software entity of the one of the software entities, invokes a corresponding alarm controller for the one of the software entities and invokes a corresponding alarm controller for a corresponding alarm controller for the parent software entity.