Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: An aspect of the present disclosure facilitates early identification of problems in execution of background processes. In one embodiment, a digital processing system characterizes the consumption of multiple resources during normal prior executions of a background process and determines a baseline pattern of consumption of resources for the background process. The system then monitors a current pattern of consumption of the resources during a current execution of the background process, and checks whether the current pattern of consumption has a deviation from the baseline pattern of consumption. The system notifies a potential problem with the current execution of the background process if a deviation is determined to exist. The notifications enable a user to get an early indication of potential problems during the execution of the background process itself.

Abstract: According to an aspect of the present disclosure, a correlation data correlating resource usage with workload signatures is maintained, each workload signature representing a cluster of block signatures, each block signature characterizing the transaction instances initiated in a corresponding block duration. For the transactions received in a current block duration, if a current block signature is not contained in the correlation data and if the current transaction arrival rate (TAR) is greater than an expected TAR, a resource requirement for the current block signature is computed. Actions to manage capacity to handle transaction instances are triggered if the resource requirement is greater than the resource allocation in the current block duration. As an unknown current block signature and a higher TAR may be indicative of a workload surge, triggering suitable actions for such block signatures facilitates such surges to be handled by the software application.

Abstract: An aspect of the present disclosure facilitates early identification of problems in execution of background processes. In one embodiment, a digital processing system characterizes the consumption of multiple resources during normal prior executions of a background process and determines a baseline pattern of consumption of resources for the background process. The system then monitors a current pattern of consumption of the resources during a current execution of the background process, and checks whether the current pattern of consumption has a deviation from the baseline pattern of consumption. The system notifies a potential problem with the current execution of the background process if a deviation is determined to exist. The notifications enable a user to get an early indication of potential problems during the execution of the background process itself.

Abstract: An aspect of the present disclosure facilitates characterizing operation of software applications having large number of components. In one embodiment, a digital processing system receives a first data indicating invocation types and corresponding invocation counts at an entry component for multiple block durations, where the entry component causes execution of internal component of the software application. The system also receives a second data indicating values for a processing metric at the internal components for the same block durations. The system then constructs for each internal component, a corresponding component model correlating the values for the processing metrics at the internal component indicated in the second data to the invocation types and invocation counts of the entry component indicated in the first data. The component models can aid in the performance management of the software application.

Abstract: An aspect of the present disclosure facilitates identifying differences in resource usage across different versions of a software application. In one embodiment, a respective first usage of resources is quantified for each of a set of workload signatures during the processing of transaction instances using a first version of a software application in a first sequence of block durations. A respective second usage of resources is quantified for each of the set of workload signatures during the processing of transaction instances using a second version of the software application in a second sequence of block durations. For each workload signature, the respective first usage and the respective second usage are compared to identify differences in the resource usage across different versions of the software application.

Abstract: An aspect of the present disclosure facilitates identifying differences in resource usage across different versions of a software application. In one embodiment, a respective first usage of resources is quantified for each of a set of workload signatures during the processing of transaction instances using a first version of a software application in a first sequence of block durations. A respective second usage of resources is quantified for each of the set of workload signatures during the processing of transaction instances using a second version of the software application in a second sequence of block durations. For each workload signature, the respective first usage and the respective second usage are compared to identify differences in the resource usage across different versions of the software application.

Abstract: According to an aspect of the present disclosure, a correlation data correlating resource usage with workload signatures is maintained, each workload signature representing a cluster of block signatures, each block signature characterizing the transaction instances initiated in a corresponding block duration. For the transactions received in a current block duration, if a current block signature is not contained in the correlation data and if the current transaction arrival rate (TAR) is greater than an expected TAR, a resource requirement for the current block signature is computed. Actions to manage capacity to handle transaction instances are triggered if the resource requirement is greater than the resource allocation in the current block duration. As an unknown current block signature and a higher TAR may be indicative of a workload surge, triggering suitable actions for such block signatures facilitates such surges to be handled by the software application.

Abstract: Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: Various techniques associated with application performance monitoring are disclosed. In some embodiments, a subset of application methods is configured to capture response time metrics, and response time metrics for a prescribed application transaction are computed by summing corresponding response time metrics of methods of the subset that are executed during each transaction invocation. Method and transaction response time metrics are collected for each of a plurality of observation intervals, and the collected response time metrics are analyzed to identify anomalous method and transaction states. Co-occurring anomalous transaction and method states are correlated to identify a set of hotspot methods for the transaction, wherein hotspot methods comprise expected root causes for anomalies of the transaction.

Abstract: Various techniques employed by an application performance management service to generate an application behavior learning based capacity forecast model are disclosed. In some embodiments, such a capacity forecast model is at least in part generated by clustering collected transaction data into one or more usage patterns, analyzing collected usage pattern data, and solving a mathematical model generated from the usage pattern data to determine a sensitivity of a resource to each type of transaction associated with an application.

Abstract: Various techniques associated with application performance monitoring are disclosed. In some embodiments, a subset of application methods is configured to capture response time metrics, and response time metrics for a prescribed application transaction are computed by summing corresponding response time metrics of methods of the subset that are executed during each transaction invocation. Method and transaction response time metrics are collected for each of a plurality of observation intervals, and the collected response time metrics are analyzed to identify anomalous method and transaction states. Co-occurring anomalous transaction and method states are correlated to identify a set of hotspot methods for the transaction, wherein hotspot methods comprise expected root causes for anomalies of the transaction.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.