ML-Based Virtual Desktop Resource Provisioning
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Solution Overview
Problem
The onboarding process for new users in enterprises is bottlenecked by the manual assessment of computational needs, and the collection of detailed user-specific data raises data privacy and security concerns, hindering efficient provisioning of virtual desktop resources.
Innovation Solution
A system utilizing machine learning algorithms, including reinforcement learning, to automatically provision virtual desktop resources based on the role of new users by analyzing telemetry data from existing users, with edge servers collecting and processing data to anonymize and compress it before training, ensuring efficient and secure resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of computational needs is performed for new users, then accurate resource provisioning can be achieved, but the onboarding process becomes bottlenecked and inefficient
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing telemetry data from existing users before new users are onboarded. Machine learning models are pre-trained on this historical data to automatically determine computational resource needs, eliminating the need for manual assessment during the onboarding process itself.
Solution Approach 2:
The system enables self-service by using automatically generated policies based on machine learning predictions to provision resources for new users without manual intervention. The ML models autonomously assess computational needs and configure appropriate virtual desktop resources, freeing administrators from manual assessment tasks.
2Productivity
If detailed user-specific telemetry data is collected and stored in a central enterprise system, then the efficiency of resource provisioning can be improved through better insights, but data privacy and security concerns are raised
Solution Approach 1:
The system applies local quality by processing and analyzing telemetry data at the edge (local to user departments or regions) rather than centralizing all raw data in a central enterprise system. This distributed approach allows efficient local insights while reducing security risks associated with centralized data storage.
Solution Approach 2:
The system extracts only the essential patterns and insights from detailed telemetry data using machine learning, rather than storing and processing all raw user-specific data. The ML models learn from historical data to generate provisioning policies without requiring access to sensitive underlying user information, thus improving efficiency while protecting privacy.
Data Source
AI summary
An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to generate, based on a given role of a plurality of roles within an enterprise, a policy for a new user assigned to the given role. The policy includes recommendations of virtual desktop resources of an enterprise system to provide to the new user. The machine learning algorithm was trained using information identifying virtual desktop resources used by a set of existing users of the system, each of whom is assigned to a role of the plurality of roles within the enterprise. The processor receives a request to provide a new user with access to the enterprise system. In response, the processor implements the machine learning algorithm to generate the policy for the new user, and subsequently provisions the new user with the one or more virtual desktop resources recommended by the policy.