VM Resource Allocation via Pseudo-Random Selection
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Solution Overview
Problem
Conventional server resource allocation methods, such as first-in, first-out protocols, lead to uneven usage of physical resources and fail to account for resource degradation over time, resulting in inefficient allocation of CPU, RAM, and disk storage to virtual machines.
Innovation Solution
Implementing a pseudo-random selection method for allocating physical resources to virtual machines, where resources near the end of their reliable lifetime are excluded from the pool, and utilizing metadata to track utilization information and total uptime for optimized resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If first-in, first-out protocol is used for resource allocation, then allocation simplicity is maintained, but resource usage becomes uneven and degradation is not accounted for
Solution Approach 1:
Instead of allocating resources in first-in, first-out order, the patent inverts the approach by using pseudo-random selection to allocate resources. This inversion prevents any single resource from being consistently overused or underused, thereby achieving more even resource utilization while maintaining allocation simplicity through the use of a standardized pseudo-random algorithm.
2Speed
If conventional resource allocation is used, then allocation speed is maintained, but resource degradation over time is not accounted for
Solution Approach 1:
The patent applies preliminary action by tracking resource utilization metrics and calculating wear levels before resources are allocated. The hypervisor monitors resource usage over time and uses this information to predict when resources may degrade. By performing this analysis in advance, the system can exclude worn resources from the allocation pool before they fail, thereby maintaining high allocation speed while improving resource reliability.
3Productivity
If resources are continuously allocated without considering wear, then allocation efficiency is maintained, but resource lifetime is reduced
Solution Approach 1:
The patent implements feedback by continuously monitoring resource utilization metrics such as the number of computations performed, read/write operations, and operational time. This feedback information is used to calculate a wear level for each resource. The hypervisor uses this wear information to dynamically adjust resource allocation, excluding heavily worn resources from the available pool. This feedback mechanism maintains allocation efficiency by keeping resources in use while extending their lifetime by preventing excessive wear through intelligent allocation decisions.
Data Source
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AI summary
A system and method include reception of a request to create a virtual machine associated with a requested number of resource units of each of a plurality of resource types, determination, for each of the plurality of resource types, of a pool of available resource units, random selection, for each of the plurality of resource types, of the requested number of resource units from the pool of available resource units of the resource type, and allocation of the selected resource units of each of the plurality of resource types to the virtual machine.