Adaptive Virtual System Provisioning for Cloud Resource Pools
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Solution Overview
Problem
Existing virtual system provisioning systems face resource-intensive delays due to fixed resource availability and peak demand, leading to inefficiencies and increased wait times for users, especially during high utilization periods.
Innovation Solution
An adaptive provisioning method that pre-provisions virtual systems based on historical data, user behavior, and resource utilization patterns, storing them in a resource pool for immediate allocation when requested, and dynamically adjusts the provisioning schedule to anticipate future demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual systems are provisioned on-demand during peak demand periods, then resource availability is improved, but provisioning time and wait times increase significantly
Solution Approach 1:
The system performs preliminary provisioning actions by pre-provisioning virtual systems during off-peak periods based on predicted future demands. Historical data and user behavior patterns are analyzed to anticipate resource needs, allowing systems to be prepared in advance rather than provisioned on-demand during peak periods, thereby eliminating wait times while ensuring resource availability.
2Speed
If more virtual systems are pre-provisioned to reduce wait times, then service speed is improved, but resource utilization efficiency deteriorates due to idle pre-provisioned systems
Solution Approach 1:
The system dynamically adjusts the provisioning schedule based on real-time resource utilization data and changing demand patterns. The pre-provisioning strategy is not static but adapts continuously, optimizing the balance between having sufficient pre-provisioned systems for quick allocation and maintaining high resource utilization efficiency by avoiding excessive idle capacity.
Solution Approach 2:
The system incorporates feedback loops that monitor resource utilization data and user behavior patterns continuously. This feedback is used to refine predictions and adjust pre-provisioning decisions, ensuring that the number of pre-provisioned systems matches actual demand patterns, thereby maintaining both fast provisioning and high resource utilization efficiency.
3Device complexity
If fixed resource allocation is used to simplify system management, then system complexity is reduced, but adaptability to peak demand and user needs deteriorates
Solution Approach 1:
The system employs self-service mechanisms through automated predictive analytics and dynamic scheduling algorithms that autonomously analyze historical data, predict future demands, and adjust provisioning strategies without manual intervention. This maintains simplicity in system management while achieving high adaptability to varying user needs and peak demand periods.
Data Source
AI summary
A method includes determining a plurality of configuration entries based on received provisioning requests and further based on provisioning system resource utilization data. Each configuration entry includes a corresponding virtual system template, and a corresponding number of virtual systems to be provisioned. The method also includes provisioning the corresponding number of virtual systems for a first configuration entry in the plurality of configuration entries. The corresponding number of virtual systems are provisioned based on the corresponding virtual system template for the first configuration entry. The method additionally includes storing the provisioned virtual systems in a resource pool and processing a provisioning request utilizing a pre-provisioned virtual system stored in the resource pool.