Virtual Machine Image Pattern Distribution for Cloud Provisioning
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Solution Overview
Problem
Cloud computing environments face challenges in achieving efficient provisioning times for virtual machines due to varying storage architectures and cloud management platforms, leading to inconsistent resource allocation and user experience.
Innovation Solution
A method to determine the distribution of virtual machine patterns across a pool of instances based on historical data, allowing for even distribution, percentage-based distribution, or user/customer-level grouping to optimize provisioning and meet demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual machines are provisioned on-demand in cloud computing environments, then resource flexibility and adaptability are improved, but provisioning time varies significantly leading to inconsistent service quality
Solution Approach 1:
The patent pre-provisions virtual machine instances from an available pool before actual user requests are processed. By anticipating demand and having resources ready in advance, the system eliminates variable provisioning delays while maintaining the flexibility to allocate resources on-demand based on actual usage patterns.
2Speed
If virtual machine instances are pre-provisioned in a pool, then provisioning speed is improved, but storage architecture complexity and management platform complexity increase
Solution Approach 1:
The patent creates a universal pool of virtual machine instances that can serve multiple different user requests and workload types. This multi-functional pool approach consolidates what would otherwise require multiple specialized storage architectures and management systems, reducing overall complexity while maintaining fast provisioning capabilities.
3Speed
If virtual machine instances are pre-provisioned in a pool, then provisioning speed is improved, but system complexity and resource management overhead increase
Solution Approach 1:
The system implements automated resource management where the virtual machine pool automatically allocates and manages instances based on demand signals. This self-service mechanism reduces the need for complex manual management systems and human intervention, allowing fast provisioning without proportionally increasing operational complexity.
4Productivity
If historical data is used to determine VM pattern distributions, then resource allocation efficiency is improved, but data processing requirements and system overhead increase
Solution Approach 1:
The patent applies historical data analysis selectively to determine VM pattern distributions only when beneficial, rather than continuously processing all available data. This partial application of data processing achieves improved resource allocation efficiency without the excessive overhead of comprehensive continuous analysis.
Data Source
AI summary
Embodiments of the present invention provide an approach for determining distributions of virtual machine (VM) patterns across pools of VM instances based upon historical data (e.g., to achieve faster provisioning times). In a typical embodiment, a total pool size for a pool of VM instances is determined (e.g., based on historical data). Then, a distribution of a set of VM instance patterns across the pool is determined (e.g., also based upon historical data). Once the distribution has been determined, the pool of VM instances may be provisioned according to the distribution. In one embodiment, the VM patterns may be evenly distributed across the pool. In another embodiment, the VM patterns may be distributed according to percentages with which the VM patterns were previously requested. In yet another embodiment, the VM patterns may be grouped into two or more groups that are associated with particular user/customer level(s) (e.g., privilege and/or permission level, a service level purchased and/or specified by the consumer, etc).