Adaptive Virtual Machine Request Handler for Cloud Resource Allocation
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Solution Overview
Problem
In large cloud computing environments, the manual approval process for virtual machine (VM) requests becomes bottlenecked, leading to increased waiting times for users due to the need for IT administrators to review thousands of requests based on predefined resource specifications, which is inefficient and labor-intensive.
Innovation Solution
An adaptive request handler (ARH) is implemented to automatically approve VM requests using a tolerance that defines an allowable deviation from preset resource specifications, which can be varied based on monitored factors such as system resource utilization and billing history, allowing for adaptive management of resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual approval process is used for VM requests, then resource allocation control is maintained, but waiting time for VM deployment increases and productivity decreases
Solution Approach 1:
The system enables self-service automatic approval for VM requests by allowing users to define their own tolerance levels for resource specification deviations. The adaptive request handler automatically evaluates requests against these user-defined tolerances and system conditions, eliminating the need for manual administrator approval while maintaining controlled resource allocation through adaptive tolerance adjustments.
Solution Approach 2:
The tolerance values are made dynamic rather than static. The system adaptively adjusts tolerance levels based on real-time system conditions such as resource availability, current workload, and user history. This dynamic adjustment allows the system to automatically approve requests when conditions permit while maintaining control when resources are constrained.
2Manufacturing precision
If manual approval process is used for VM requests, then resource specification precision is maintained, but administrator workload increases and productivity decreases
Solution Approach 1:
Users self-configure tolerance ranges for their VM requests, defining how much deviation from preset resource specifications they are willing to accept. This shifts the responsibility of precision control to the users while automating the evaluation process, eliminating repetitive administrator work while maintaining specification precision through user-defined parameters.
Solution Approach 2:
The system implements feedback loops where the adaptive request handler continuously monitors system conditions, user request patterns, and resource utilization. This feedback enables automatic adjustment of approval decisions based on current system state, maintaining resource specification precision through adaptive control rather than static manual review.
3Loss of time
If automatic approval with fixed tolerance is implemented, then waiting time is reduced, but adaptability to changing system conditions decreases
Solution Approach 1:
The system transitions from fixed tolerance values to dynamic, adaptive tolerance levels. The adaptive request handler continuously adjusts tolerance ranges based on real-time system conditions such as resource availability, current demand, and historical usage patterns. This enables automatic approval to remain both fast and adaptable to changing environments.
Solution Approach 2:
Real-time feedback mechanisms monitor system state changes and automatically adjust approval tolerances accordingly. When system conditions change (e.g., resource availability increases or demand patterns shift), the feedback loop triggers adaptive tolerance adjustments, allowing the automatic approval system to respond dynamically to new conditions while maintaining rapid processing.
4Loss of time
If tolerance is increased for automatic approval, then more requests are approved automatically reducing waiting time, but resource allocation control precision decreases
Solution Approach 1:
Instead of using a single fixed tolerance value, the system employs dynamic tolerance adjustment where the tolerance level changes based on system conditions. When resources are abundant, higher tolerances allow more automatic approvals with reduced waiting time. When resources are constrained, tolerances tighten to maintain precise control over resource allocation, automatically balancing speed and precision.
Solution Approach 2:
The system changes the tolerance parameter dynamically rather than maintaining a constant value. The adaptive request handler modifies tolerance levels as a function of system state, user history, and resource availability. This parameter change enables the system to optimize between approval speed and control precision by adjusting the tolerance parameter in response to changing conditions.
Data Source
AI summary
An adaptive request handler (ARH) receives a virtual machine (VM) request from a user and determines whether to automatically approve the VM request using a tolerance that defines an allowable amount of deviation from preset resource specifications. In some embodiments, the ARH adaptively varies the tolerance based on one or more monitored factors, such as an aggregate system resource utilization by and/or a billing history of the user or a group that includes the user. In some embodiments, the VM request is based on a template selected by the user from among a plurality of templates eligible for automatic approval, wherein a plurality of tolerances each defines an allowable amount of deviation from preset resource specifications of a respective one of the eligible templates. The ARH may, in some embodiments, vary each of the plurality of tolerances independently based on one or more monitored factors.