Cloud API Key Segmentation for Large-Scale Task Processing
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Solution Overview
Problem
Cloud service providers limit resource allocation through cloud API keys, preventing large-scale tasks like genome analysis from being processed in cloud computing systems due to resource usage constraints.
Innovation Solution
A method and apparatus for allocating resources in a cloud computing system using multiple cloud API keys, where a resource management apparatus determines the necessary resource amount for a task, selects appropriate API keys from a table, and requests resource allocation to execute the task, allowing for large-scale task processing despite resource limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud service providers limit resource allocation through cloud API keys, then resource usage control is improved, but large-scale task processing capability deteriorates
Solution Approach 1:
The patent segments the resource allocation process by dividing a single API key into multiple API keys, each with specific resource quotas. This allows the system to maintain control through individual key limits while enabling large-scale tasks by coordinating multiple keys together. The resource management apparatus manages multiple API keys separately, tracking usage for each key independently while aggregating their capacity for large tasks.
2Productivity
If multiple cloud API keys are used for resource allocation, then large-scale task processing capability is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces a resource management apparatus as an intermediary between the user and the cloud computing system. This intermediary automatically manages multiple API keys, handling the complexity of key selection, resource tracking, and allocation coordination. Users interact with a simplified interface while the resource management apparatus handles the underlying complexity of multiple API keys in the background.
Solution Approach 2:
The resource management apparatus implements self-service mechanisms by automatically selecting appropriate API keys based on task requirements, monitoring resource usage across multiple keys, and dynamically allocating resources without requiring manual user intervention for each key management decision. The system autonomously manages the complexity of multiple API keys.
3Reliability
If resource allocation is limited per API key, then resource management control is improved, but task execution flexibility deteriorates
Solution Approach 1:
The patent makes the resource management apparatus multi-functional by enabling it to simultaneously maintain strict resource control through individual API key limits while providing flexible task execution across diverse workloads. The system can adapt to different task types (genome analysis, machine learning, data processing) by dynamically selecting and combining appropriate API keys, thus achieving both control and flexibility.
Data Source
AI summary
The present disclosure relates to allocating a resource in a cloud computing system to a task using multiple cloud application programming interface (API) keys that allow an access to the cloud computing system. An allocation method includes receiving a request for a task from the client terminal, determining a resource amount necessary for the task, selecting multiple cloud API keys for allocation of the determined resource amount, requesting resource allocation corresponding to the determined resource amount to the cloud computing system using the selected multiple cloud API keys, and being allocated with a resource in the cloud computing system, and executing the task requested from the client terminal using the allocated cloud resource.