Capacity Risk Indicator for Virtual Machine Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing capacity risks in virtual machine deployments is challenging due to difficulties in accurately assessing and predicting hardware resource requirements, leading to potential service level failures and inefficient resource allocation.
Innovation Solution
A system that collects access assignment data and calculates a capacity risk indicator using a semiconductor processor to assess the risk of meeting prospective demands, providing managers with timely and accurate capacity risk assessments to optimize resource allocation and prevent saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware resources are allocated to meet prospective capacity demand, then service level reliability is improved, but resource allocation efficiency deteriorates due to inaccurate capacity assessment
Solution Approach 1:
The system performs preliminary capacity risk assessment by collecting access assignment data and calculating capacity risk indicators before capacity issues occur. This allows proactive resource allocation adjustments to prevent service level failures while optimizing resource usage efficiency.
Solution Approach 2:
The system continuously monitors capacity risk by collecting access assignment data and calculating capacity risk indicators, providing feedback to administrators about hardware resource allocation status. This feedback mechanism enables data-driven decisions to balance reliability and allocation efficiency.
2Measurement precision
If detailed capacity assessment is performed, then capacity risk accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts and collects specific access assignment data from hypervisor operations, focusing only on the data elements necessary for capacity risk assessment. This extraction approach provides accurate capacity risk measurement without requiring complex comprehensive monitoring of all system parameters.
Solution Approach 2:
The system replaces complex manual capacity assessment mechanisms with automated data collection and calculation processes. The capacity risk indicator is calculated automatically based on collected access assignment data, eliminating the need for complex manual analysis while maintaining high measurement precision.
3Reliability
If capacity risk monitoring is implemented, then service level reliability is improved, but operational complexity increases
Solution Approach 1:
The system performs self-monitoring of capacity risk by automatically collecting access assignment data and calculating capacity risk indicators without requiring manual intervention. This self-service capability maintains service level reliability while simplifying operational complexity by eliminating manual monitoring tasks.
Solution Approach 2:
The capacity risk indicator acts as an intermediary metric that translates complex hardware resource allocation patterns into a simple, actionable risk assessment. This intermediary approach allows administrators to monitor service level reliability without dealing with the operational complexity of underlying resource allocation details.
Data Source
AI summary
An access data collector collects access assignment data characterizing active access assignment operations of a hypervisor in assigning host computing resources among virtual machines for use in execution of the virtual machines. Then, a capacity risk indicator calculator calculates a capacity risk indicator characterizing a capacity risk of the host computing resources with respect to meeting a prospective capacity demand of the virtual machines, based on the access assignment data.