Cloud Region Provisioning via Automated Resource Scoring
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Solution Overview
Problem
Current cloud-based systems face inefficiencies in provisioning and managing cloud regions for virtual desktops, leading to unnecessary costs and potential lack of availability of virtual resources.
Innovation Solution
A system that automatically provisions and manages cloud regions by using a monitoring service to collect usage data and a resource management engine to determine scores for sets of cloud regions, allowing for the selection of the optimal set for providing virtual desktops to client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud regions are manually provisioned and managed, then system control and configuration flexibility are maintained, but operational efficiency decreases and unnecessary costs are incurred
Solution Approach 1:
The system implements self-service automation where the cloud desktop service system automatically provisions, manages, and optimizes cloud regions without requiring manual intervention. The resource management engine autonomously monitors usage data, evaluates cloud region performance, and executes provisioning decisions based on predetermined criteria, eliminating the need for human operators to manually configure and manage cloud infrastructure.
2Reliability
If multiple cloud regions are provisioned to ensure availability, then service reliability improves, but resource utilization efficiency deteriorates leading to unnecessary costs
Solution Approach 1:
The system dynamically adjusts cloud region provisioning based on real-time monitoring of usage data and changing service demands. The resource management engine continuously evaluates which cloud regions should be active or inactive, transitioning the infrastructure from a static to a dynamic state that adapts to current needs, thereby maintaining service availability while optimizing resource utilization and reducing unnecessary costs.
Solution Approach 2:
The system changes operational parameters by automatically provisioning or de-provisioning specific cloud regions based on evaluated scores derived from usage data. This parameter change approach allows the system to maintain service reliability by keeping necessary regions active while deactivating underutilized regions, thus improving resource utilization efficiency without compromising service availability.
3Loss of energy
If automated resource management is implemented, then cost efficiency improves, but the complexity of the management system increases
Solution Approach 1:
The resource management engine is designed as a universal multi-functional component that handles multiple tasks including monitoring usage data, evaluating cloud region scores, making provisioning decisions, and executing automated management actions. By consolidating these diverse functions into a single versatile engine, the system achieves cost efficiency through automation while minimizing the increase in overall system complexity.
4Reliability
If cloud regions are provisioned in advance to ensure availability, then service reliability improves, but resource costs increase due to idle capacity
Solution Approach 1:
The system performs preliminary evaluation and scoring of cloud regions based on historical usage data and predetermined criteria before actual provisioning decisions are made. This preliminary action allows the system to identify which cloud regions are likely to be needed, enabling proactive provisioning of necessary regions while avoiding advance provisioning of unnecessary regions, thus maintaining availability while reducing idle capacity costs.
Data Source
AI summary
A system and method for selecting Cloud regions for supporting a virtual desktop on a client device is disclosed. The system includes a plurality of available Cloud regions that each include resources for providing a virtual desktop to a client device of a user via a network. A monitoring service is coupled to the available cloud regions. The monitoring service collects use data from the available cloud regions in relation to providing virtual desktops to other client devices. A resource management engine is coupled to the available cloud regions. The resource management engine determines a score for each of a plurality of sets of the Cloud regions in relation to the client device. A control plane selects a set of Cloud regions from the plurality of sets according to the determined scores of each of the sets to provide the virtual desktop to the client device.