Cloud Resource Interpreter for Automated Provisioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing approaches for managing computing resources in cloud environments are inefficient and inaccurate, particularly when dynamically embedding or implementing resources in virtual machine images and tools.
Innovation Solution
A system comprising an interpreter component with a request processor, provisioning queue, execution engine, results store, and response marshaller/handler collaborates to interpret resource requests, generate tree structures, submit jobs, and store results, enabling efficient management of computing resources such as virtual machines, storage volumes, and network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user interface or command line approaches are used for cloud resource management, then ease of operation is maintained, but productivity and accuracy deteriorate
Solution Approach 1:
The system enables automated self-service through embedded interpreters that automatically parse resource expressions, generate provisioning requests, and manage cloud resources without manual intervention. Virtual machine images contain built-in interpretation capabilities that allow automatic resource provisioning based on expressed needs.
Solution Approach 2:
Manual mechanical operations (clicking UI elements, typing commands) are replaced by an automated interpretation system. The interpreter component processes resource expressions programmatically, substituting manual user actions with automated computational processes that parse, analyze, and execute resource provisioning.
2Measurement precision
If existing manual approaches are used for resource management, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
An intermediary interpreter component is introduced between the resource expression and the provisioning system. This intermediary parses and validates resource expressions, ensuring accurate interpretation of resource requirements before submission to the provisioning queue, thereby improving precision without exposing end users to complexity.
Solution Approach 2:
The system is segmented into distinct functional components: expression parsing, tree structure generation, query resolution, job submission, and result storage. Each component handles a specific aspect of resource provisioning, improving accuracy through specialized processing while organizing complexity into manageable modular segments.
3Productivity
If automated interpretation system is implemented, then productivity and accuracy improve, but device complexity increases
Solution Approach 1:
The interpreter component is designed as a universal system that handles multiple resource types (compute, storage, network) through a single unified expression parsing mechanism. This multi-functional approach consolidates complexity into one versatile component rather than requiring separate processing systems for each resource type.
Solution Approach 2:
The system transitions from linear sequential processing to a tree-structured representation of resource expressions. By converting linear resource definitions into hierarchical tree structures, the system enables parallel query resolution and more efficient processing pathways, improving productivity while managing computational complexity.
Data Source
AI summary
An approach for querying and managing/manipulating network computing resources (e.g., cloud computing resources) is provided. Typically, an interpreter component may be implemented so that a language describing such computing resources may be interpreted. The interpreter component may include other components such as a request processor, a provisioning queue, an execution engine, a results store, and response marshaller/handler that collaborate to manage the computing resources. Such computing resources may, for example, include virtual machine instances, virtual machine images, storage volumes, network resources, files, software bundles, etc.