Intelligent change window planner
This disclosure describes techniques and mechanisms for determine a change window of least impact based on the type of activity, urgency, and preference, and highlighting risk(s) of choosing a change window. The techniques streamline and automate change window technology and provide customized and personalized change window option(s) to an administrator of a network.
1. A method implemented at least in part by a controller of a network, comprising:
receiving, from a device within the network, data input by a network administrator, the data corresponding to implementing a change window and comprising an urgency associated with the change window and one or more user preferences of the network administrator associated with implementing the change window;
determining, by the controller and based at least in part on the data, one or more windows of least impact;
determining, by the controller and based at least in part on the data, one or more risks associated with implementing the change window during each of the one or more windows of least impact, the one or more risks including one or more applications impacted by each of the one or more windows of least impact and a number of users impacted by each of the one or more windows of least impact; and
sending, to the device, a message for display to the network administrator, the message including the one or more windows of least impact and the one or more risks associated with each window of least impact of the one or more windows of least impact and enabling the network administrator to provide input selecting a window of least impact.
2. The method of claim 1 , further comprising:
monitoring, by the controller, code running on one or more network elements;
identifying, by the controller, keywords associated with the code, the keywords indicating one or more bugs that require an upgrade; and
sending, to the device, an indication that the upgrade is required.
3. The method of claim 1 , wherein the one or more risks further include one or more of:
an indication to notify the number of users.
4. The method of claim 1 , wherein the one or more windows of least impact are further based at least in part on traffic load data, application traffic, and user data.
5. The method of claim 1 , wherein at least one of the one or more windows of least impact or the one or more risks are determined using one or more machine learning models.
6. The method of claim 1 , further comprising:
receiving, by the controller and from the device, an indication of a selection of a window of least impact of the one or more windows of least impact; and
storing, by the controller, the indication in a database associated with a service provider of the network, the indication being correlated with a user profile of the network administrator.
7. The method of claim 1 , wherein the one or more risks identify an application associated with a particular window of least impact, the method further comprising:
determining that the application corresponds to a priority application; and
refraining from including the particular window of least impact in the one or more windows of least impact.
8. A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, from a device within a network, data input by a network administrator, the data corresponding to implementing a change window;
determining, based at least in part on the data, one or more windows of least impact;
determining, based at least in part on the data, one or more risks associated with each of the one or more windows of least impact, the one or more risks identifying an identifier of an application impacted by implementing the change window during a window of least impact of the one or more windows of least impact and a number of users impacted;
sending, to the device, a message for display, the message including the one or more windows of least impact, the identifier of the application, and the one or more risks associated with each of the one or more windows of least impact;
receiving, from the device, second data input by the network administrator, the second data indicating a selection of one of the one or more windows of least impact; and
implementing the change window based on the second data.
9. The system of claim 8 , wherein the data comprises a type of change associated with at least one element within the network, an urgency associated with the change window, and one or more user preferences of the network administrator.
10. The system of claim 8 , the operations further comprising:
monitoring code running on one or more network elements;
identifying keywords associated with the code, the keywords indicating one or more bugs that require an upgrade; and
sending, to the device, an indication that the upgrade is required.
11. The system of claim 8 , wherein the one or more risks further identify the number of users impacted by the window of least impact, and wherein the message further comprises the number of users.
12. The system of claim 8 , wherein the one or more windows of least impact are further based at least in part on traffic load data, application traffic, and user data.
13. The system of claim 8 , wherein at least one of the one or more windows of least impact or the one or more risks are determined using one or more machine learning models.
14. The system of claim 8 , the operations further comprising:
storing the indication in a database associated with a service provider of the network, the indication being correlated with a user profile.
15. One or more non-transitory computer-readable media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving, from a device within a network, data input by a network administrator, the data corresponding to implementing a change window;
determining, based at least in part on the data, one or more windows of least impact;
determining, based at least in part on the data, one or more risks associated with implementing the change window during each of the one or more windows of least impact, the one or more risks including one or more applications impacted by each of the one or more windows of least impact and indications of users impacted by each of the one or more windows of least impact; and
sending, to the device, a message for display to the network administrator, the message including the one or more windows of least impact, the one or more applications, and the one or more risks and enabling the network administrator to select a window of least impact to implement the change window.
16. The one or more non-transitory computer-readable media of claim 15 , wherein the data comprises a type of change associated with at least one element within the network, an urgency associated with the change window, and one or more user preferences of the network administrator.
17. The one or more non-transitory computer-readable media of claim 15 , wherein the indications comprise a number of users impacted by each of the one or more windows of least impact, and wherein the message further includes the number of users.
18. The one or more non-transitory computer-readable media of claim 15 , wherein the one or more windows of least impact are further based at least in part on traffic load data, application traffic, and user data.
19. The one or more non-transitory computer-readable media of claim 15 , wherein at least one of the one or more windows of least impact or the one or more risks are determined using one or more machine learning models.
20. The one or more non-transitory computer-readable media of claim 15 , the operations further comprising:
receiving, from the device, an indication of a selection of a window of least impact of the one or more windows of least impact; and
storing the indication in a database associated with a service provider of the network, the indication being correlated with a user profile.