Automated Model-Based Resource Provisioning for Virtual Desktops
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Solution Overview
Problem
Managing resource provisioning in distributed computing services and virtual desktop systems is inefficient due to static allocation of servers and virtual machines, leading to over-provisioning, under-utilization, and manual management, which limits scalability and flexibility in meeting varying user demands.
Innovation Solution
An automated management system that dynamically provisions servers and virtual machines using model-based provisioning, allowing for the creation, configuration, and management of virtual machines based on demand, with tools that automatically select and configure servers, manage resource pools, and update images for efficient resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If servers are statically pre-allocated and pre-configured to meet predetermined quality of service requirements, then service reliability is improved, but resource utilization deteriorates due to over-provisioning and under-utilization
Solution Approach 1:
The system dynamically provisions and de-provisions virtual machine instances based on real-time demand monitoring. When demand increases, new instances are automatically created and loaded with images; when demand decreases, instances are de-provisioned and images are unloaded, transforming the static resource allocation into a dynamic system that adapts to changing conditions while maintaining QoS requirements.
Solution Approach 2:
The system performs preliminary actions by pre-loading images onto servers in advance before they are needed. This allows rapid provisioning of virtual machine instances when demand arises, as the infrastructure and images are already prepared. Conversely, images are unloaded when not needed, preventing waste of storage and memory resources.
2Stability of the object's composition
If virtual machines are statically assigned to users and servers, then system stability is improved, but adaptability deteriorates when user demand varies or servers fail
Solution Approach 1:
The system implements self-service through automated monitoring and provisioning. When a server fails or demand changes, the system automatically detects the condition, re-provisions virtual machine instances to appropriate servers, and manages image loading/unloading without manual intervention. This maintains system stability through automated failover while adapting to changing conditions.
Solution Approach 2:
The system continuously monitors server status, user demand, and resource utilization, using this feedback to dynamically adjust virtual machine provisioning. When servers fail or demand patterns change, the feedback loop triggers automatic re-provisioning and image management actions, enabling the system to maintain stability while adapting to varying conditions.
3Manufacturing precision
If manual configuration and management of virtual machines is performed, then provisioning precision is improved, but productivity deteriorates due to time-consuming manual tasks
Solution Approach 1:
The system automates the entire virtual machine provisioning process, including monitoring demand, selecting appropriate servers, loading images, creating instances, and managing de-provisioning. This self-service automation maintains provisioning accuracy through consistent application of provisioning rules while dramatically improving management efficiency by eliminating manual configuration tasks.
Solution Approach 2:
The system replaces manual mechanical provisioning operations with automated software-based processes. Image loading, virtual machine creation, server selection, and de-provisioning are all performed automatically through software agents and orchestration, substituting manual administrative actions with automated computational processes that maintain precision while improving speed and efficiency.
4Manufacturing precision
If images are manually copied and virtual machines are manually created and started, then configuration control is improved, but loss of time increases due to manual operations
Solution Approach 1:
The system performs preliminary actions by automatically copying images to servers and preparing virtual machine configurations in advance based on predicted or monitored demand. This preliminary image preparation and automated instance creation maintains configuration control through consistent automated processes while reducing provisioning time by eliminating manual copying and setup operations.
Solution Approach 2:
The system implements self-service automation for image management and virtual machine provisioning. Automated agents monitor demand, retrieve images, copy them to appropriate servers, and create/start virtual machine instances without human intervention. This maintains configuration control through automated rule-based processes while dramatically reducing the time required for provisioning compared to manual operations.
Data Source
AI summary
Resources are provisioned in an automated manner for shared services in a resource-on-demand system. A model representing an observed state of resources in the resource-on-demand system allocated to the shared services and a model representing a desired state of the shared services are stored. At least one policy applicable to provisioning the resources for the shared services is determined. The policy and information from the models are applied to automatically provision the resources for satisfying the desired state of the shared services.