Federated Edge Intelligence for Virtual Desktop Resource Slicing
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Solution Overview
Problem
The onboarding process for new users in enterprise systems is bottlenecked by the manual assessment of computational needs, which raises data privacy and security concerns due to the collection and storage of detailed user-specific data in a central enterprise system.
Innovation Solution
Implementing a machine learning algorithm trained on telemetry data from existing users to automatically provision virtual desktop resources based on user roles, using edge servers to collect and process data anonymously before transmitting it to the internal system, thereby anonymizing and compressing the data for efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of computational needs is performed during onboarding, then resource provisioning accuracy can be improved, but onboarding efficiency deteriorates due to bottlenecks
Solution Approach 1:
The system enables self-service through automated machine learning models that independently assess computational needs and provision resources without manual intervention. The ML model automatically analyzes user roles, department information, and historical data to generate resource recommendations and execute provisioning, eliminating the manual assessment bottleneck while maintaining accurate resource allocation
Solution Approach 2:
The patent replaces the mechanical manual assessment process with an automated machine learning system. The ML model substitutes human analysts by automatically processing user information, evaluating computational requirements, and making provisioning decisions through algorithmic analysis rather than manual review, thereby improving both speed and consistency
2Measurement precision
If detailed user-specific telemetry data is collected and stored centrally, then resource assessment accuracy can be improved, but data privacy and security risks increase
Solution Approach 1:
The system implements local quality by processing and analyzing telemetry data at distributed edge locations rather than centralizing all raw data. Edge devices perform local computation to extract meaningful patterns and generate anonymized insights, maintaining data privacy while preserving the analytical value needed for accurate resource assessment
Solution Approach 2:
The patent introduces an intermediary layer of anonymization and aggregation between data collection and central storage. Edge devices act as intermediaries that strip personally identifiable information from telemetry data before transmission, and aggregate individual user patterns into generalized models, thereby enabling accurate resource assessment without exposing sensitive user information
3Productivity
If automated machine learning provisioning is implemented, then onboarding efficiency is improved, but system complexity increases due to ML model deployment
Solution Approach 1:
The system applies segmentation by dividing the ML provisioning system into modular components: edge devices for local data processing, anonymization services for privacy protection, model training pipelines for continuous improvement, and deployment infrastructure for model distribution. This modular architecture manages complexity by allowing independent development and maintenance of each component while maintaining overall system functionality
Data Source
AI summary
An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.