Cluster Data Resource Cost Evaluation via Storage and Computing Indices
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Solution Overview
Problem
There is a pressing need for a method to evaluate the cost of cluster data resources in big data scenarios, as existing methods fail to effectively assess the reasonable usage of cluster hardware resources.
Innovation Solution
The method involves obtaining evaluation dimensions for cluster data resources, which include storage and computing evaluation dimensions. It then calculates storage and computing evaluation indices, such as invalid and low-efficiency resource proportions, to assess the cost of data storage and computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cluster hardware resources are used to implement various service scenarios, then the functionality and versatility of the system is improved, but the cost of cluster data resources increases and resource usage efficiency becomes difficult to evaluate
Solution Approach 1:
The patent implements a feedback mechanism by calculating resource usage evaluation indices (invalid resource proportion and low-efficiency resource proportion) and feeding them back to evaluate overall resource usage efficiency. This allows the system to monitor and adjust resource allocation based on actual performance, preventing unnecessary cost increases while maintaining service versatility.
Solution Approach 2:
The patent changes the evaluation parameters from simple resource consumption metrics to composite indices that include invalid resource proportion and low-efficiency resource proportion. This parameter transformation enables more accurate assessment of resource usage efficiency, allowing the system to maintain adaptability while controlling costs through informed resource management decisions.
2Measurement precision
If comprehensive resource usage tracking is implemented to evaluate cost, then the measurement precision of resource evaluation is improved, but the device complexity increases
Solution Approach 1:
The patent segments the resource evaluation into two distinct dimensions: storage resource evaluation and computing resource evaluation. Each dimension has its own evaluation index and calculation method. This segmentation allows for precise measurement of each resource type while keeping the overall system manageable by breaking down the complex evaluation task into smaller, independent modules.
3Productivity
If resource evaluation metrics are calculated and analyzed, then the productivity of resource management is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary action by pre-defining the evaluation index structure, including invalid resource proportion and low-efficiency resource proportion, before actual resource usage occurs. This allows the system to quickly calculate and evaluate resource efficiency without extensive real-time processing, thereby improving resource management productivity while minimizing time loss.
Data Source
AI summary
Embodiments of the present disclosure provide a method for evaluating a cost of a cluster data resource, a storage medium, and an electronic device. And the method includes: a storage evaluation index in a storage evaluation dimension and a computing evaluation index in a computing evaluation dimension for evaluating a cost of a cluster data resource are obtained. The data storage resource cost is evaluated based on an index value of the storage evaluation index, and the data computing resource cost is evaluated based on an index value of the computing evaluation index. In this way, invalid use and low-efficiency use in the cluster data resource are distinguished, so that the evaluation of the cost of the cluster data resource is more accurate.