Server Decommissioning Timing Using Discrete Cost Curves
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Solution Overview
Problem
Existing methods for determining server decommissioning in cloud service platforms are inefficient due to the difficulty in defining continuous functions for capex and maintenance cost curves, leading to inaccurate timing recommendations and potential waste from decommissioning healthy servers.
Innovation Solution
Utilizing discrete functions for maintenance cost curves and incorporating server additional value and replacement server cost lines to determine optimal decommissioning timing based on multi-modal data, including capex, usage age, and historical data to recommend different decommissioning actions at various times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous functions are used for capex and maintenance cost curves, then mathematical analysis can be performed, but it is difficult to define accurate continuous functions leading to inaccurate timing recommendations
Solution Approach 1:
The patent segments the continuous cost curves into discrete time-point data points. Instead of attempting to define complex continuous functions, the system collects maintenance cost data at specific time intervals and server additional value at discrete usage age points, transforming the continuous function problem into a discrete data analysis problem that is easier to define and more accurate to measure.
2Ease of manufacture
If servers are decommissioned at predefined lifetime, then replacement planning is simplified, but healthy servers may be decommissioned causing waste
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors actual maintenance costs and server performance, then uses this feedback to dynamically adjust decommissioning timing recommendations. The system compares actual maintenance cost curves against server additional value curves, providing feedback that prevents premature decommissioning of healthy servers while still maintaining structured replacement planning through discrete time-point analysis.
3Ease of manufacture
If discrete functions are used instead of continuous functions, then data definition becomes easier, but mathematical analysis capability is reduced
Solution Approach 1:
The patent changes the parameter representation from continuous mathematical functions to discrete parameter sets at specific time points. By defining maintenance costs and server additional value at discrete usage age intervals, the system maintains sufficient analytical capability to determine optimal decommissioning timing while dramatically simplifying data collection and definition requirements.
Data Source
AI summary
The present disclosure provides methods and apparatuses for providing timing recommendation of server decommissioning in a cloud service platform. Multi-modal data associated with decommissioning-decision made to a target server in the cloud service platform may be obtained. A maintenance cost curve of the target server and at least one of a server additional value curve of the target server and a replacement server cost line of a replacement server may be generated based on the multi-modal data. Decommissioning timing recommendation of the target server may be determined according to the maintenance cost curve and at least one of the server additional value curve and the replacement server cost line.