Cloud Service Resource Provider Selection via Best-Fit Model
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Solution Overview
Problem
Current cloud service management systems are limited in their ability to dynamically select specific resource providers at runtime, leading to inflexible service designs and manual configuration complexities, which hinder efficient resource utilization and management.
Innovation Solution
Implementing a generic resource provider that allows for dynamic selection of specific resource providers based on Quality-Of-Service (QoS), business policies, and contextual considerations using a best-fit model, enabling abstraction of complexities and separation of concerns between roles and functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration is used for deploying cloud services, then service deployment can be performed, but configuration complexity and time consumption increase
Solution Approach 1:
The system enables self-service through automated resource provider selection. The cloud service manager automatically selects appropriate resource providers based on service requirements and policies without manual intervention, allowing the system to configure and deploy services autonomously, thus reducing both configuration complexity and time consumption
Solution Approach 2:
The system performs preliminary action by pre-configuring service blueprints with resource provider selection criteria and policies before actual service deployment. This advance preparation includes defining quality-of-service parameters, business policies, and contextual considerations that guide automatic resource provider selection, eliminating the need for manual configuration during deployment
2Ease of manufacture
If specific resource providers are hardcoded in service designs, then service deployment is straightforward, but system flexibility and adaptability decrease
Solution Approach 1:
The system implements dynamics by transitioning from static, hardcoded resource provider assignments to dynamic selection based on service requirements. The cloud service manager evaluates quality-of-service parameters, business policies, and contextual factors at runtime to automatically select the most appropriate resource providers, enabling the system to adapt to changing conditions while maintaining deployment simplicity
Solution Approach 2:
The system achieves universality through service blueprints that define generic resource provider requirements rather than specific provider implementations. This allows the same service blueprint to work with multiple different resource providers by selecting the most appropriate one based on current conditions, making the system both easy to deploy and highly adaptable
3Productivity
If automated resource provider selection is implemented, then system efficiency improves, but service design complexity increases
Solution Approach 1:
The system introduces an intermediary layer—the cloud service manager—that handles the complexity of automated resource provider selection. This intermediary translates service requirements into resource provider selection decisions based on quality-of-service parameters, business policies, and contextual considerations, thereby improving resource utilization efficiency while shielding service designers from the underlying complexity
4Measurement precision
If manual configuration is used for cloud service management, then service control is precise, but management time and operational complexity increase
Solution Approach 1:
The system implements feedback mechanisms where the cloud service manager continuously monitors service performance and resource provider status. Based on this feedback, the system automatically adjusts resource provider selections to maintain precise service control while operating autonomously, thereby reducing management time without sacrificing control precision
Data Source
AI summary
Selecting resources for a cloud service can include defining a specific resource provider constraint parameter, determining a parameter value for the specific resource provider constraint parameter, analyzing a plurality of specific resource providers and selecting a specific resource provider from the plurality of available specific resource providers based on the analysis and using a best-fit model.