Edge Service Allocation in Large-Scale Processing Framework Clusters
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Solution Overview
Problem
In large-scale data processing environments, it is cumbersome to generate configuration information for edge services to communicate with large-scale processing clusters, making it difficult to efficiently allocate and manage resources.
Innovation Solution
A method that identifies requests for large-scale processing clusters, determines compatible edge services based on cluster type and version, and generates a user interface to select and initiate both the cluster and edge services, providing necessary configuration information for communication and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration of edge services is implemented, then flexibility and control are improved, but complexity and time consumption increase
Solution Approach 1:
The system performs preliminary actions by automatically generating configuration information for edge services before deployment. The configuration generator component creates necessary configuration data based on selected edge services and processing frameworks, eliminating the need for manual configuration steps and reducing deployment time.
Solution Approach 2:
The system implements self-service through automated configuration generation. When a user selects an edge service and processing framework combination, the system automatically generates the required configuration information without human intervention, allowing the deployment process to serve itself rather than requiring manual configuration efforts.
2Productivity
If automated configuration generation is implemented, then efficiency is improved, but system complexity increases
Solution Approach 1:
The configuration generator acts as an intermediary component between the edge service selection and the actual deployment. It translates high-level service selections into detailed configuration information, managing the complexity internally while presenting a simple interface to users. This mediator approach enables automated configuration without exposing system complexity to end users.
Solution Approach 2:
The configuration generator is designed as a universal component that handles multiple edge services and processing framework combinations through a single automated process. Rather than requiring separate configuration mechanisms for each service type, the universal generator adapts to different combinations, reducing overall system complexity while maintaining high productivity.
3Adaptability or versatility
If comprehensive edge service options are provided, then adaptability is improved, but user interface complexity increases
Solution Approach 1:
The user interface implements local quality by dynamically adapting the displayed edge service options based on the selected processing framework. When a user selects a specific framework, the interface locally adjusts to show only compatible edge services, providing comprehensive adaptability while maintaining interface simplicity by filtering options contextually rather than presenting all possible combinations at once.
Data Source
AI summary
Systems, methods, and software described herein enhance the generation of large-scale processing framework clusters and corresponding edge services. In one implementation, a method includes identifying a request for a large-scale processing cluster, and identifying one or more edge services based on the type and version of the large-scale processing cluster. The method further provides generating a user interface that indicates the one or more edge services available, receiving a selection of at least one edge service in the one or more edge services, and initiating execution of the large-scale processing framework cluster and the at least one edge service, wherein the at least one edge service is provided with configuration information for the large-scale processing framework cluster.