Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of identifying at least one computing specification image within a plurality of computing specification images; monitoring each data agent within the plurality of preinstalled data agents for a predetermined period of time to establish a data agent usage baseline associated with each data agent within the plurality of preinstalled data agents; utilizing a chaos engineering algorithm to dynamically perturb each data agent; calculating a usage test score for each data agent within the plurality of preinstalled data agents; calculating an overall data agent-specific usage score associated with each data agent within the plurality of preinstalled data agents based on the plurality of data agent-specific usage test scores; and rejecting at least one data agent within the plurality of preinstalled data agents from being utilized to launch the instance of the software application.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining monitoring compliance for an enterprise application deployed on an application delivery platform. Existing resources, monitors and alerts are discovered with gaps in monitoring being calculated based on a comparison of monitoring objectives and the existing monitors and alerts. Gaps in monitoring are reported in a GUI reflecting effectiveness of existing monitors.

Abstract: A method including receiving, from a user device, a user request to access data associated with a web page; generating, by a processor, a first transaction identification; collecting transaction information, the transaction information comprising server-side metrics; integrating, by the processor, the first transaction identification with the transaction information; transmitting, by the processor, the first transaction identification to the user device; receiving, from the user device, client-side data associated with a second transaction identification; integrating, by the processor, the server-side metrics and the client-side data; and analyzing, by the processor, the integrated server-side metrics and the client-side data.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining monitoring compliance for an enterprise application deployed on an application delivery platform. Existing resources, monitors and alerts are discovered with gaps in monitoring being calculated based on a comparison of monitoring objectives and the existing monitors and alerts. Gaps in monitoring are reported in a GUI reflecting effectiveness of existing monitors.

Abstract: In some implementations, a device may cause execution of a batch processing job. The batch processing job may be associated with an application and at least one user. The device may determine that execution of the batch processing job is associated with one or more execution errors. The device may determine, based on the one or more execution errors, an error remediation action for the batch processing job. The device may cause performance of the error remediation action for the batch processing job.

Abstract: An exemplary system, method, and computer-accessible medium for classifying data associated with an application performance management (“APM”) agent(s) deployed on a server(s), can include, for example, receiving first information related to data to be read by the APM agent(s) located at the server(s), generating second information by assigning a score to the first information using machine learning procedure(s), classifying the data based on the second information. A data plugin(s) associated with the data can be determined based on the classification. A determination can be made as to whether the data is labeled as a data type. The second information can be generated based on (i) a log(s) associated with the data, (ii) transaction data associated with the data, (iii) server metrics associated with the data, (iv) a time stamp(s) associated with the data, (v) infrastructure data associated with the data, (vi) domain knowledge, or (vii) environment knowledge.

Abstract: A method including receiving, from a user device, a user request to access data associated with a web page; generating, by a processor, a first transaction identification; collecting transaction information, the transaction information comprising server-side metrics; integrating, by the processor, the first transaction identification with the transaction information; transmitting, by the processor, the first transaction identification to the user device; receiving, from the user device, client-side data associated with a second transaction identification; integrating, by the processor, the server-side metrics and the client-side data; and analyzing, by the processor, the integrated server-side metrics and the client-side data.

Abstract: A method including receiving, from a user device, a user request to access data associated with a web page; generating, by a processor, a first transaction identification; collecting transaction information, the transaction information comprising server-side metrics; integrating, by the processor, the first transaction identification with the transaction information; transmitting, by the processor, the first transaction identification to the user device; receiving, from the user device, client-side data associated with a second transaction identification; integrating, by the processor, the server-side metrics and the client-side data; and analyzing, by the processor, the integrated server-side metrics and the client-side data.

Abstract: An exemplary system, method, and computer-accessible medium for classifying data associated with an application performance management (“APM”) agent(s) deployed on a server(s), can include, for example, receiving first information related to data to be read by the APM agent(s) located at the server(s), generating second information by assigning a score to the first information using machine learning procedure(s), classifying the data based on the second information. A data plugin(s) associated with the data can be determined based on the classification. A determination can be made as to whether the data is labeled as a data type. The second information can be generated based on (i) a log(s) associated with the data, (ii) transaction data associated with the data, (iii) server metrics associated with the data, (iv) a time stamp(s) associated with the data, (v) infrastructure data associated with the data, (vi) domain knowledge, or (vii) environment knowledge.

Abstract: A method including receiving, from a user device, a user request to access data associated with a web page; generating, by a processor, a first transaction identification; collecting transaction information, the transaction information comprising server-side metrics; integrating, by the processor, the first transaction identification with the transaction information; transmitting, by the processor, the first transaction identification to the user device; receiving, from the user device, client-side data associated with a second transaction identification; integrating, by the processor, the server-side metrics and the client-side data; and analyzing, by the processor, the integrated server-side metrics and the client-side data.

Abstract: A method including receiving, from a user device, a user request to access data associated with a web page; generating, by a processor, a first transaction identification; collecting transaction information, the transaction information comprising server-side metrics; integrating, by the processor, the first transaction identification with the transaction information; transmitting, by the processor, the first transaction identification to the user device; receiving, from the user device, client-side data associated with a second transaction identification; integrating, by the processor, the server-side metrics and the client-side data; and analyzing, by the processor, the integrated server-side metrics and the client-side data.