Abstract: Systems and methods for identifying and evicting idle sessions include training a machine learning model as a session classifying model to learn rules for classifying active sessions between clients and the cloud-based service. The session classifying model is trained to receive a plurality of parameters pertaining to the document associated with an active session as input and to apply the rules to the plurality of parameters to determine a classification for the active session and to provide an output indicative of the classification for the active session. The session classifying model is then utilized in the cloud-based service to classify the active sessions. The active sessions classified as idle sessions may then be evicted from the cloud-based service.

Abstract: Various embodiments of the present technology include an improved system for measuring the impacts of feature rollouts on machine utilization and service performance. More specifically, embodiments of the present technology include an exposure control system based on randomized machine assignments and a corresponding score card for comparing machine utilization metrics. In an embodiment, a computing apparatus identifies a new feature in a codebase, wherein the codebase has been updated with the new feature across multiple resources, enables the new feature for a target group of the multiple resources while keeping the new feature dormant for a control group of the multiple resources, collects performance information for the target group and the control group for a time period, and generates a visualization of one or more differences between the performance information for the target group and the performance information for the control group for the time period.