Resource Prestaging for Rapid Virtual Machine Deployment
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Solution Overview
Problem
Data centers face inefficiencies in provisioning computing resources, particularly in reducing the time between requesting and receiving a virtual machine with an operating system, due to the lack of pre-staged resources and inefficient resource allocation.
Innovation Solution
Implementing a service management system that pre-stages computing resources by restoring an operating system image to a volume, assigning processing resources, and preparing the instance before disconnecting it, allowing for rapid deployment of virtual machines when requested by clients, with fallback options for unmet demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing resources are provisioned on-demand after client request, then resource allocation flexibility is improved, but deployment time increases
Solution Approach 1:
The system performs preliminary actions by pre-staging computing resources (virtual machine images, containers, or sandboxes) in a ready-to-use state before client requests arrive. Resources are prepared in advance with operating systems installed and configured, then quickly allocated to clients when needed, resolving the contradiction between on-demand flexibility and fast deployment.
2Speed
If computing resources are pre-staged in advance, then deployment speed is improved, but resource storage requirements increase
Solution Approach 1:
The system creates copies of computing resources (virtual machine images, containers, or sandboxes) that can be pre-staged and stored efficiently. These copied resource templates are maintained in a library and can be rapidly instantiated and allocated to multiple clients, achieving fast deployment while managing storage through efficient copying rather than duplicating full resource sets.
3Device complexity
If resource provisioning is performed manually, then system complexity is reduced, but productivity decreases
Solution Approach 1:
The system implements self-service automation where computing resources automatically provision, configure, and allocate themselves without manual intervention. The system autonomously manages the entire workflow from resource preparation to client allocation, significantly improving productivity while maintaining manageable complexity through automated orchestration.
4Reliability
If computing resources are allocated to specific clients immediately, then client service quality is improved, but resource utilization efficiency decreases
Solution Approach 1:
The system creates universal computing resources that can serve multiple clients and purposes. Pre-staged resources are maintained in a pool and can be dynamically allocated to different clients based on demand, ensuring high service quality for each client while maximizing overall resource utilization through multi-functional resource sharing and flexible reassignment.
Data Source
AI summary
A service management system prepares computing resources for use before a client requests the resource. For example, a service management system of a provider may restore an operating system image to a volume. The service management may then assign a processing resource to the volume to form a virtual machine. The service management system may cause the processing resource to finish preparation of the operating system on the volume. The service management system may disconnect the resulting staged volume from the processing resource. The staged volume may remain active in the low-latency data store and be added to a list of available staged volumes having the operating system. When a client requests a virtual machine having an operating system, the service management system may determine that the client receives the staged volume as part of the virtual machine to avoid the start-up costs of preparing a volume.