Resource Allocation via Role-Based Proximity Ranking
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Solution Overview
Problem
Administering computing resources is complex due to the need for balanced configurations and fault tolerance, with existing techniques varying in success, often resulting in suboptimal performance or increased latency due to incorrect configurations and shared hardware failures.
Innovation Solution
A provisioning system infers the role of proposed computing resources based on user-specified data, ranking available implementation resources according to proximity conditions to optimize placement and avoid shared hardware failures, thereby ensuring better resource allocation and redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If servers are spread further apart to reduce shared hardware failures, then fault tolerance is improved, but latency increases and bandwidth decreases
Solution Approach 1:
The system applies different proximity requirements to different resource types based on their roles. Critical resources that need high availability are placed farther apart, while resources requiring low latency are placed closer together. This differentiated placement strategy resolves the contradiction by not applying a uniform distance rule to all resources.
Solution Approach 2:
The system changes the placement parameter (distance between resources) based on the inferred roles of resources. By analyzing resource names and configurations to infer roles, the system dynamically adjusts proximity parameters to optimize both fault tolerance and performance for different resource types.
2Reliability
If redundant servers are provisioned to ensure fault tolerance, then reliability is improved, but device complexity increases
Solution Approach 1:
The system automatically infers resource roles from user-specified data and autonomously determines optimal placement without requiring manual configuration of redundancy settings. This self-service approach reduces configuration complexity while maintaining fault tolerance through automated role-based placement decisions.
Solution Approach 2:
The system performs preliminary role inference and placement optimization before resource provisioning begins. By pre-determining the appropriate placement strategy based on inferred roles, the system avoids complex configuration tasks during deployment and simplifies the overall provisioning process.
3Speed
If resources are placed closer together to reduce latency, then performance is improved, but the risk of shared hardware failures increases
Solution Approach 1:
The system applies differentiated placement strategies where critical resources requiring high availability are separated, while resources prioritizing performance are co-located. This local quality approach allows simultaneous optimization of both reliability and performance for different resource categories without uniform constraints.
Solution Approach 2:
The system dynamically adjusts the placement parameter (distance) based on inferred resource roles and performance requirements. By changing proximity parameters according to role-based analysis, the system optimizes the balance between latency and failure risk for each specific resource configuration.
Data Source
AI summary
A provisioning system may select an available implementation resource by analyzing user data to infer a role of a proposed computing resource and comparing the role to roles of other existing computer resources. The user data may include a user-specified identifier for the proposed computing resource, such as a label. The role may be used to rank available implementation resources using factors including distance to, roles of, and relationship with other user computing resources. A ranking of available implementation resources may be used to select an implementation resource for provisioning the proposed computing resource.