Cloud Resource Allocation Using Kendall Tau Meter Selection
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Solution Overview
Problem
Cloud service providers face challenges in accurately estimating cloud-based resource consumption for future periods due to the lack of objective evaluation methods for meter accuracy, leading to potential under or over allocation of resources.
Innovation Solution
A resource system that selects the most accurate meter from a set based on Kendall Tau coefficient and Copula-based index to determine consumption distributions, allowing for improved resource allocation by computing an approximate Shapley value to attribute wasted resources fairly among client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional meters are used to measure cloud-based resource consumption, then resource allocation can be performed, but the accuracy of the measured values cannot be evaluated
Solution Approach 1:
The system introduces a feedback mechanism by comparing meter measurements against actual consumption data and using this feedback to evaluate and select the most accurate meters through Kendall Tau coefficient calculation, enabling continuous improvement of measurement accuracy
Solution Approach 2:
The patent introduces an intermediary evaluation framework that uses consumption distributions and statistical coefficients as mediators between raw meter data and resource allocation decisions, allowing indirect but systematic assessment of meter accuracy
2Adaptability or versatility
If multiple meters are used to measure different types of consumption, then more comprehensive resource tracking is achieved, but the complexity of selecting the most accurate meter increases
Solution Approach 1:
The system transforms the complex task of meter selection into a parameter optimization problem by calculating Kendall Tau coefficients and consumption distributions, converting subjective meter evaluation into an objective parameter-based selection process
Solution Approach 2:
The patent replaces manual or heuristic meter selection mechanisms with an automated statistical evaluation system that uses mathematical coefficients and algorithms to objectively determine the most accurate meters
3Productivity
If resource allocation is based on estimated consumption values, then future resource needs can be predicted, but the accuracy of predictions decreases when meter values do not accurately reflect actual consumption
Solution Approach 1:
The system performs preliminary evaluation and selection of the most accurate meters before using their data for resource allocation predictions, ensuring that the foundation for accurate predictions is established in advance
Solution Approach 2:
The system uses its own consumption data and statistical methods to automatically evaluate and select meters, with the selected meters then serving the system's resource allocation needs without external intervention
Data Source
AI summary
In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.