Abstract: Examples described herein relate to method and an issue management system for handling issues reported from network devices. The issue management system may receive an issue from a network device of a plurality of network devices arranged in one or more computing environments. The issue management system may determine whether there exists a solution corresponding to the issue in a solution repository based on the issue and a knowledge base. Further, in response to determining that there exists the solution corresponding to the issue, the issue management system may communicate the solution to a computing environment of the one or more computing environments that hosts the network device reporting the issue. Alternatively, if there exists no solution corresponding to the issue, the issue management system may relay the issue to a management station for the management station to address the issue.

Abstract: Examples described herein relate to method and an issue management system for handling issues reported from network devices. The issue management system may receive an issue from a network device of a plurality of network devices arranged in one or more computing environments. The issue management system may determine whether there exists a solution corresponding to the issue in a solution repository based on the issue and a knowledge base. Further, in response to determining that there exists the solution corresponding to the issue, the issue management system may communicate the solution to a computing environment of the one or more computing environments that hosts the network device reporting the issue. Alternatively, if there exists no solution corresponding to the issue, the issue management system may relay the issue to a management station for the management station to address the issue.

Abstract: Example implementations relate to proactively protecting service endpoints based on deep learning of user location and access patterns. A machine-learning model is trained to recognize anomalies in access patterns relating to endpoints of a cloud-based service by capturing metadata associated with user accesses. The metadata for a given access includes information regarding a particular user that initiated the given access, a particular device utilized, a particular location associated with the given access and specific workloads associated with the given access. An anomaly relating to an access by a user to a service endpoint is identified by monitoring the access patterns and applying the machine-learning model to metadata associated with the access. Based on a degree of risk to the cloud-based service associated with the identified anomaly, a mitigation action is determined. The cloud-based service is proactively protected by programmatically applying the determined mitigation action.