Services Resource Consumption Determination via Metadata Parsing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the capability to accurately determine services resource consumption for computing systems composed of multiple services based on system architecture and associated metadata, failing to provide comprehensive insights into expected lifetime, operational cost, and maintenance needs.
Innovation Solution
A computer-implemented method that receives a system architecture, parses it to generate resource consumption representation requests, processes these requests using resource utilization tools, and determines resource consumption by aggregating responses, employing a natural language processing machine learning model to analyze metadata and generate requests tailored to specific services and configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems are used to determine services resource consumption, then the process is simple, but the measurement precision and comprehensiveness of resource consumption data is insufficient
Solution Approach 1:
The system segments the resource consumption analysis by separating different services into distinct metadata datasets (e.g., compute service metadata, database service metadata, storage service metadata). Each service is processed independently through dedicated utilization tools, allowing precise measurement of resource consumption for each service component without interference from other services.
Solution Approach 2:
The patent introduces metadata datasets as intermediary structures that bridge the gap between system architecture representations and resource consumption measurements. These metadata datasets serve as standardized intermediaries that contain service-specific information and enable consistent processing across different services through a unified framework.
2Measurement precision
If comprehensive metadata datasets are collected for each service, then the measurement precision improves, but the quantity of data and processing complexity increases
Solution Approach 1:
The system applies local quality by creating service-specific metadata datasets that contain only the relevant attributes for each particular service type. Rather than collecting all possible data uniformly across all services, each service receives customized metadata structures tailored to its specific resource consumption characteristics, reducing unnecessary data collection while maintaining measurement precision.
3Productivity
If multiple services are analyzed simultaneously, then the productivity of resource consumption determination improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent implements universality through a unified processing framework that can handle multiple service types through a common architecture. The system uses a standardized set of utilization tools and metadata formats that work across different service types, allowing simultaneous analysis of multiple services without requiring separate specialized processing systems for each service.
4Adaptability or versatility
If historical system architectures are used for training, then the adaptability of the system improves, but the loss of time for training and data processing increases
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring historical system architecture data into standardized metadata formats before it is needed for training. This preliminary organization of historical data reduces the time required during actual training operations, as the data is already prepared in the required format and structure.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are provided. For example, a computer-implemented method provided herein may include receiving a system architecture. In some embodiments, the system architecture is representative of a plurality of services and a plurality of services metadata datasets. In some embodiments, the computer-implemented method may include parsing the system architecture to generate a plurality of services resource consumption representation requests. In some embodiments, the computer-implemented method may include processing the plurality of services resource consumption representation requests using a plurality of services resource consumption utilization tools. In some embodiments, the computer-implemented method may include receiving a plurality of services resource consumption responses from the plurality of services resource consumption utilization tools. In some embodiments, the computer-implemented method may include determining a services resource consumption based at least in part on the plurality of services resource consumption responses.