Consumption Unit Estimation Stack for Cloud Resource Analytics
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Solution Overview
Problem
Cloud computing resources often remain underutilized due to lack of visibility and control for users, leading to reduced utilization efficiency in cloud computing systems.
Innovation Solution
A consumption unit estimation (CUE) stack is introduced, which analyzes computing resource utilization tracking data, application execution tracking data, and consumption metric data to generate a consumption preview, filtering out preservable and correctable resources for prescriptive utilization processing, thereby optimizing resource allocation and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cloud computing resources are allocated to users, then users gain access to computing resources, but resource utilization efficiency decreases due to lack of visibility and control
Solution Approach 1:
The system implements feedback by continuously monitoring computing resource utilization metrics and providing this information back to users through a user interface. This enables users to see real-time or historical usage data, understand their consumption patterns, and make informed decisions about resource allocation, thereby improving utilization efficiency while maintaining ease of access
Solution Approach 2:
The system enables self-service by allowing users to independently monitor their own resource consumption, view utilization reports, and adjust their resource allocation decisions based on the provided analytics. This empowers users to optimize their own resource usage without requiring manual intervention from administrators, thus improving overall system utilization while maintaining user autonomy
2Productivity
If comprehensive resource tracking data is collected, then resource utilization efficiency improves through better analytics, but system complexity increases
Solution Approach 1:
The analytics system is segmented into distinct functional modules: a data collection component that gathers utilization metrics, a processing component that analyzes the data, and a presentation component that displays results to users. This modular segmentation reduces overall system complexity by allowing each component to be developed, maintained, and scaled independently while still achieving comprehensive resource tracking and improved utilization efficiency
Data Source
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AI summary
A multi-layer consumption unit estimation (CUE) stack may generate a consumption preview for prescribing cloud computing resource utilization. An input layer of the CUE stack may obtain computing resource utilization tracking data, consumption metric data, application execution tracking data, and computing resource reservation data for a set of computing resources. A configuration layer of the CUE stack may determine a CUE interval and determine consumption metric modifiers for a selected identity associated with the set of computing resources. A CUE engine layer may generate a consumption preview by advancing a dynamic consumption credit input/output flow analysis and executing a direct utilization consumption determination. The pre- prescriptive analysis layer may apply a filter metric to mark the preservable computing resource and the correctable computing resource for exclusion from prescriptive utilization processing and initiate the prescriptive utilization processing on the set of computing resources without computing resources marked for exclusion.