Cloud Resource Allocation via Pre-configured Virtual Machine Images
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Solution Overview
Problem
Cloud computing resources often face challenges in allocating computing capacity efficiently, as existing configurations may not meet customer demands, leading to suboptimal resource utilization and increased costs due to inefficient reconfiguration and downtime.
Innovation Solution
The resource management application within the cloud computing system reconfigures computing devices to accommodate customer requests based on value assessments, including price, lifetime value, and trust levels, prioritizing requests that offer the highest profitability and meeting customer requirements while minimizing reconfiguration costs and downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are reallocated to meet customer demands, then resource utilization efficiency is improved, but reconfiguration costs and downtime increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring and maintaining standby computing resources that can be quickly activated. Virtual machine images are pre-prepared and stored ready for deployment, allowing rapid allocation without extensive reconfiguration time when customer demands arise.
Solution Approach 2:
The system creates and maintains copies of virtual machine images and computing resource configurations that can be rapidly deployed. Instead of reconfiguring existing resources, pre-made copies are activated, significantly reducing reconfiguration downtime while maintaining high resource utilization efficiency.
2Productivity
If computing resources are reallocated to meet customer demands, then resource utilization efficiency is improved, but reconfiguration costs increase
Solution Approach 1:
The system uses pre-configured virtual machine images and resource templates that can be rapidly copied and deployed. This eliminates the need for expensive, time-consuming custom reconfiguration work, reducing reconfiguration costs while maintaining high resource utilization through flexible allocation.
Solution Approach 2:
The system changes parameters by using configurable virtual machine images with adjustable specifications (CPU, memory, storage). These standardized images can be quickly instantiated with different parameters to meet varying customer demands without requiring expensive custom hardware reconfiguration.
3Loss of energy
If computing resources are allocated based on multiple value factors, then revenue maximization is improved, but allocation complexity increases
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor customer value factors (lifetime value, trust level, price willingness) and automatically adjust resource allocation decisions. This automated feedback loop maximizes revenue by prioritizing high-value customers while managing allocation complexity through algorithmic decision-making rather than manual processes.
Solution Approach 2:
The allocation system operates autonomously by automatically evaluating customer requests against value factors and making allocation decisions without extensive human intervention. This self-service capability maximizes revenue optimization while keeping operational complexity manageable through automated valuation and decision algorithms.
Data Source
AI summary
Disclosed are various embodiments for allocating computing resources. A request to allocate a computing resource in a collection of networked computing devices is obtained. It is determined whether the request can be fulfilled according to a current configuration of the networked computing devices. A reconfiguration of one or more of the networked computing devices to a different configuration is initiated in order to fulfill the request. The reconfiguration is initiated when a value associated with the request exceeds a cost associated with fulfilling the request. The reconfiguration is initiated in response to determining that the request cannot be fulfilled according to the current configuration.