Abstract: A computer-implemented method includes providing a user interface (UI) that allows a user to select an existing database having cloud storage volumes that contain historical data of one or more customers. A first command input on the UI replicates the existing database is to create one or more new databases. Each of the new databases has new cloud storage volumes created by taking a snapshot of a corresponding one of the volumes of the existing database. Each new database contains the historical data of the existing database such that a new set of databases results. A mapping algorithm is executed in a cloud-computing instance that maps the historical data of each of the one or more customers to produce a shard map. The mapping algorithm includes a round-robin partitioning of the historical data striped by date in a circular order across the new set of databases.

Abstract: A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. The load test consists of a plurality of virtual users simulated by one or more load servers that execute a test composition on one or more webpages of the website. A waterfall chart is produced on the analytic dashboard that includes a plurality of timeline bars. Each of the timeline bars represents an aggregated result for a resource of the one or more webpages across all of the virtual users. The timeline bars change in real-time as the load test progresses.

Abstract: A method for real-time analysis of results from a load test performed on a target website includes calculating first-level aggregated test results within each of a plurality of load server instances that generate a load on the target website. The first-level aggregated test results are calculated from data points received by each of the load server instances from the target website. The first-level aggregated test results include a sum of the data points, a count of the number of the data points, a sum of squares of the data points, and an average of the data points. A standard deviation result (STDEV) is calculated and chart is generated on a display via a graphical user interface. The chart provides a visual representation of a performance metric for the load test based on the standard deviation result.

Abstract: A method includes providing a graphical user interface (GUI) that displays a view of the globe as from outer space. The view including a geographic distribution of beacons having one or more visual attributes. Each beacon corresponds to one or more real users who are accessing a website, web application or mobile app from a geographic location. The visual attributes include an indicator of a performance metric representative of an experience of the one or more real users of the website, web application, or mobile app. Each beacon is generated in real-time from aggregated data obtained in real-time from one or more corresponding real user sessions on the website, web application or mobile app. The beacons are periodically refreshed with the visual attributes changing in response to measured changes in the geographic distribution, a total number of real users at the location, or the performance indicator at the location.

Abstract: A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. The load test consists of a plurality of virtual users simulated by one or more load servers that execute a test composition on one or more webpages of the website. A waterfall chart is produced on the analytic dashboard that includes a plurality of timeline bars. Each of the timeline bars represents an aggregated result for a resource of the one or more webpages across all of the virtual users. The timeline bars change in real-time as the load test progresses.

Abstract: A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. The load test consists of a plurality of virtual users simulated by one or more load servers that execute a test composition on one or more webpages of the website. A waterfall chart is produced on the analytic dashboard that includes a plurality of timeline bars. Each of the timeline bars represents an aggregated result for a resource of the one or more webpages across all of the virtual users. The timeline bars change in real-time as the load test progresses.

Abstract: A method includes providing a graphical user interface (GUI) that displays a view of the globe as from outer space. The view including a geographic distribution of beacons having one or more visual attributes. Each beacon corresponds to one or more real users who are accessing a website, web application or mobile app from a geographic location. The visual attributes include an indicator of a performance metric representative of an experience of the one or more real users of the website, web application, or mobile app. Each beacon is generated in real-time from aggregated data obtained in real-time from one or more corresponding real user sessions on the website, web application or mobile app. The beacons are periodically refreshed with the visual attributes changing in response to measured changes in the geographic distribution, a total number of real users at the location, or the performance indicator at the location.

Abstract: A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. Responsive to input of a user on the GUI, the input comprising selection of a source chart and a target chart, a single chart is automatically generated that represents either a combination or a statistical correlation of the source and target charts. The single chart has a left y-axis and an x-axis. The combination or the statistical correlation of the single chart changing in real-time as the load test progresses. A visual representation of the single chart is then produced on the analytic dashboard.

Abstract: A method for real-time capture of actual user experiences on a website, web application or mobile app includes receiving, in first servers, one or more beacons, each containing data items. Each beacon is generated in real-time from a user session. The data items are aggregated in one or more first sets of N data buckets associated with each of the first servers. Each of the first servers transmits the data items of each of the N data buckets to a second server over a network, which further aggregates the data items received in one or more sets of N data buckets corresponding to and identically configured as the data buckets of the first servers. The corresponding data contents of the one or more second sets of N data buckets is aggregated into one or more final sets of N data buckets used in generating a real-time analytic dashboard.

Abstract: A method includes providing a graphical user interface (GUI) that displays a view of the globe as from outer space. The view including a geographic distribution of beacons having one or more visual attributes. Each beacon corresponds to one or more real users who are accessing a website, web application or mobile app from a geographic location. The visual attributes include an indicator of a performance metric representative of an experience of the one or more real users of the website, web application, or mobile app. Each beacon is generated in real-time from aggregated data obtained in real-time from one or more corresponding real user sessions on the website, web application or mobile app. The beacons are periodically refreshed with the visual attributes changing in response to measured changes in the geographic distribution, a total number of real users at the location, or the performance indicator at the location.

Abstract: A method for real-time analysis of results from a load test performed on a target website includes periodically computing first-level aggregated test results within each of a plurality of load server instances that generate a load on the target website. The first-level aggregated test results are computed from data received by each of the load server instances from the target website every first time interval. The first-level aggregated test results are then periodically sent from each of the load server instances to an associated one of a plurality of analytic server instances every second time interval. The first-level aggregated test results are aggregated by each of the analytic server instances to produce second-level aggregated test results, which test results may then further aggregated to produce third-level aggregated test results at a data storage instance in real-time.

Abstract: A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. The load test consists of a plurality of virtual users simulated by one or more load servers that execute a test composition on one or more webpages of the website. A waterfall chart is produced on the analytic dashboard that includes a plurality of timeline bars. Each of the timeline bars represents an aggregated result for a resource of the one or more webpages across all of the virtual users. The timeline bars change in real-time as the load test progresses.

Abstract: A method for real-time analysis of results from a load test performed on a target website includes calculating first-level aggregated test results within each of a plurality of load server instances that generate a load on the target website. The first-level aggregated test results are calculated from data points received by each of the load server instances from the target website. The first-level aggregated test results include a sum of the data points, a count of the number of the data points, a sum of squares of the data points, and an average of the data points. A standard deviation result (STDEV) is calculated and chart is generated on a display via a graphical user interface. The chart provides a visual representation of a performance metric for the load test based on the standard deviation result.

Abstract: A method for real-time analysis of results from a load test performed on a target website includes calculating first-level aggregated test results within each of a plurality of load server instances that generate a load on the target website. The first-level aggregated test results are calculated from data points received by each of the load server instances from the target website. The first-level aggregated test results include a sum of the data points, a count of the number of the data points, a sum of squares of the data points, and an average of the data points. A standard deviation result (STDEV) is calculated and chart is generated on a display via a graphical user interface. The chart provides a visual representation of a performance metric for the load test based on the standard deviation result.

Abstract: A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. The load test consists of a plurality of virtual users simulated by one or more load servers that execute a test composition on one or more webpages of the website. A waterfall chart is produced on the analytic dashboard that includes a plurality of timeline bars. Each of the timeline bars represents an aggregated result for a resource of the one or more webpages across all of the virtual users. The timeline bars change in real-time as the load test progresses.

Abstract: A method for real-time capture of actual user experiences on a website, web application or mobile app includes receiving, in first servers, one or more beacons, each containing data items. Each beacon is generated in real-time from a user session. The data items are aggregated in one or more first sets of N data buckets associated with each of the first servers. Each of the first servers transmits the data items of each of the N data buckets to a second server over a network, which further aggregates the data items received in one or more sets of N data buckets corresponding to and identically configured as the data buckets of the first servers. The corresponding data contents of the one or more second sets of N data buckets is aggregated into one or more final sets of N data buckets used in generating a real-time analytic dashboard.

Abstract: A method for real-time capture of actual user experiences on a website, web application or mobile app includes receiving, in first servers, one or more beacons, each containing data items. Each beacon is generated in real-time from a user session. The data items are aggregated in one or more first sets of N data buckets associated with each of the first servers. Each of the first servers transmits the data items of each of the N data buckets to a second server over a network, which further aggregates the data items received in one or more sets of N data buckets corresponding to and identically configured as the data buckets of the first servers. The corresponding data contents of the one or more second sets of N data buckets is aggregated into one or more final sets of N data buckets used in generating a real-time analytic dashboard.

Abstract: A method includes providing a graphical user interface (GUI) that displays a view of the globe as from outer space. The view including a geographic distribution of beacons having one or more visual attributes. Each beacon corresponds to one or more real users who are accessing a website, web application or mobile app from a geographic location. The visual attributes include an indicator of a performance metric representative of an experience of the one or more real users of the website, web application, or mobile app. Each beacon is generated in real-time from aggregated data obtained in real-time from one or more corresponding real user sessions on the website, web application or mobile app. The beacons are periodically refreshed with the visual attributes changing in response to measured changes in the geographic distribution, a total number of real users at the location, or the performance indicator at the location.

Abstract: An automated method for provisioning a grid used to run a load test on a target website includes sending one or more requests in a multi-threaded manner to at least one cloud provider, the one or more requests for an allocation of N load server instances and M result server instances which comprise the grid. Requests received back from the cloud provider are also handled in a multi-threaded manner; any errors occurring during the allocation being corrected automatically. The N load server instances and the M result server instances are then verified to be operational and correctly running software deployed to provide defined test services. Errors identified during the verification are automatically corrected either by attempting to restart a failed instance or allocating a different instance.

Abstract: An automated method for provisioning a grid used to run a load test on a target website includes sending one or more requests in a multi-threaded manner to at least one cloud provider, the one or more requests for an allocation of N load server instances and M result server instances which comprise the grid. Requests received back from the cloud provider are also handled in a multi-threaded manner; any errors occurring during the allocation being corrected automatically. The N load server instances and the M result server instances are then verified to be operational and correctly running software deployed to provide defined test services. Errors identified during the verification are automatically corrected either by attempting to restart a failed instance or allocating a different instance.