Systems and methods for troubleshooting and performance analysis of cloud-based services

An apparatus comprising:

• a processor and memory storing instructions that, when executed, cause the processor to execute an analyzer service that performs steps of
• receiving metrics over time from a plurality of analyzers, wherein the metrics include service-related metrics and network-related metrics related to a cloud-based service, wherein the plurality of analyzers are located and executed at a plurality of user devices and in the cloud-based service, and wherein analyzers executed in the cloud-based service are adapted to communicate with analyzers executed at user devices, wherein the communication includes instructions for operating the analyzers executed at user devices based on metrics collected at analyzers executed in the cloud-based service;
• analyzing the metrics to determine a status of the cloud-based service over the time and processing results received from analyzers executed at the user devices and the cloud-based service over the time, wherein the results can include geographic location of user devices and associated nodes in a cloud-based system, processing latency introduced by the cloud-based system, response time, application performance in the cloud-based system, and service availability of the cloud-based system;
• identifying issues related to the cloud-based service based on a comparison of the metrics received over the time, wherein the issues include any of an issue on a particular user device, an issue in a network between a particular user device and the cloud service, and an issue within the cloud service;
• predicting network congestion utilizing big data predictive learning techniques to discover patterns and relationships for the prediction of the network congestion, wherein the metrics can be used to develop a historical view of the cloud-based system to be analyzed by the predictive learning techniques;
• and
• determining upgrades in network capacity, processing capacity, and geographic locations of the cloud-based system based on the identified issues and historical view.

A method comprising:

• receiving metrics over time from a plurality of analyzers, wherein the metrics include service-related metrics and network-related metrics related to a cloud-based service, wherein the plurality of analyzers are located and executed at a plurality of user devices and in the cloud-based service, and wherein analyzers executed in the cloud-based service are adapted to communicate with analyzers executed at user devices, wherein the communication includes instructions for operating the analyzers executed at user devices based on metrics collected at analyzers executed in the cloud-based service;
• analyzing the metrics to determine a status of the cloud-based service over the time and processing results received from analyzers executed at the user devices and the cloud-based service over the time, wherein the results can include geographic location of user devices and associated nodes in a cloud-based system, processing latency introduced by the cloud-based system, response time, application performance in the cloud-based system, and service availability of the cloud-based system;
• identifying issues related to the cloud-based service based on a comparison of the metrics received over the time, wherein the issues include any of an issue on a particular user device, an issue in a network between a particular user device and the cloud service, and an issue within the cloud service;
• predicting network congestion utilizing big data predictive learning techniques to discover patterns and relationships for the prediction of the network congestion, wherein the metrics can be used to develop a historical view of the cloud-based system to be analyzed by the predictive learning techniques;
• and
• determining upgrades in network capacity, processing capacity, and geographic locations of the cloud-based system based on the identified issues and historical view.