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

1. A non-transitory computer-readable storage medium having computer readable code stored thereon for programming a device to perform 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.

2. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further include

detecting a performance degradation related to the cloud-based service at a particular location via the analyzers located and executed in the cloud-based service;

instructing, based on detecting the performance degradation, some or all user devices at the location to operate their respective analyzer via the communication between analyzers executed on the cloud-based service and analyzers executed on the user devices; and

analyzing bi-directional results from the plurality of analyzers executed at the user devices and the cloud-based service.

3. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further include

detecting a performance degradation related to the cloud-based service at a particular location; and

adding network or processing capacity based thereon.

4. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further include

detecting a performance degradation related to a particular user device due to any of misconfigurations, out-of-date software, and hardware deficiencies; and

causing performance of a remedial action based on the performance degradation.

5. The non-transitory computer-readable storage medium of claim 1 , wherein the metrics include a combination of metrics collected from analyzers executed on the cloud-based service and analyzers executed on the user devices including processing latency introduced by the cloud-based service, response times for one or more requests through the cloud-based service, application performance metrics, and service availability of the cloud-based service.

6. The non-transitory computer-readable storage medium of claim 1 , wherein, for a particular user device, the metrics include webload functionality with and without the cloud-based service for a comparison thereof.

7. The non-transitory computer-readable storage medium of claim 1 , wherein, for a particular user device, the user device is configured to operate through the cloud-based service except when collection of the metrics is performed by a respective analyzer operating thereon.

8. The non-transitory computer-readable storage medium of claim 1 , wherein each of the plurality of analyzers are configured to operate at a certain frequency for collection of the metrics.

9. The non-transitory computer-readable storage medium of claim 1 , wherein the plurality of analyzers are configured to operate at different intervals to provide a comprehensive view over the time.

10. The non-transitory computer-readable storage medium of claim 1 , wherein the metrics include results from ping tests and traceroute tests to detect host latency and latency per hop in a network, and the metrics include a response time for loading a web page associated with one or more Uniform Resource Locators (URLs).

11. 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.

12. The apparatus of claim 11 , wherein the steps further include

detecting a performance degradation related to the cloud-based service at a particular location via the analyzers located and executed in the cloud-based service;

instructing, based on detecting the performance degradation, some or all user devices at the location to operate their respective analyzer via the communication between analyzers executed on the cloud-based service and analyzers executed on the user devices; and

analyzing bi-directional results from the plurality of analyzers executed at the user devices and the cloud-based service.

13. The apparatus of claim 11 , wherein the steps further include

detecting a performance degradation related to the cloud-based service at a particular location; and

adding network or processing capacity based thereon.

14. The apparatus of claim 11 , wherein the steps further include

detecting a performance degradation related to a particular user device due to any of misconfigurations, out-of-date software, and hardware deficiencies; and

causing performance of a remedial action based on the performance degradation.

15. The apparatus of claim 11 , wherein the metrics include a combination of metrics collected from analyzers executed on the cloud-based service and analyzers executed on the user devices including processing latency introduced by the cloud-based service, response times for one or more requests through the cloud-based service, application performance metrics, and service availability of the cloud-based service.

16. The apparatus of claim 11 , wherein, for a particular user device, the metrics include webload functionality with and without the cloud-based service for a comparison thereof.

17. 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.

18. The method of claim 17 , further comprising

detecting a performance degradation related to the cloud-based service at a particular location via the analyzers located and executed in the cloud-based service;

instructing, based on detecting the performance degradation, some or all user devices at the location to operate their respective analyzer via the communication between analyzers executed on the cloud-based service and analyzers executed on the user devices; and

analyzing bi-directional results from the plurality of analyzers executed at the user devices and the cloud-based service.

19. The method of claim 17 , further comprising

detecting a performance degradation related to the cloud-based service at a particular location; and

adding network or processing capacity based thereon.

20. The method of claim 17 , further comprising

detecting a performance degradation related to a particular user device due to any of misconfigurations, out-of-date software, and hardware deficiencies; and

causing performance of a remedial action based on the performance degradation.