Metadata Tag-Based Resource Allocation in Distributed Data Centers
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Solution Overview
Problem
Current data center management systems lack efficient automated methods for optimizing resource allocation across multiple data centers based on metadata tags, leading to suboptimal placement and allocation of virtualized compute and storage resources.
Innovation Solution
A global orchestrator uses metadata tags to select the appropriate data center for allocating resources by identifying objects within a resource object model that meet specific criteria, enabling automated management and optimization of resource placement and allocation across multiple data centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated resource allocation using metadata tags is implemented, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a global orchestrator as an intermediary component that manages resource allocation across multiple data centers. The orchestrator uses metadata tags and resource policies to automatically determine optimal resource placement, reducing manual intervention while maintaining controlled complexity through centralized management rather than distributed complexity across all systems
Solution Approach 2:
The system changes the state of resource management by introducing metadata tags with specific criteria (such as location, latency requirements) that transform how resources are identified and allocated. This parameter-based approach enables automated decision-making based on tagged attributes rather than manual configuration, improving efficiency while keeping the underlying system architecture relatively simple
2Device complexity
If manual resource placement methods are used, then system complexity is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The system enables self-service automated resource allocation where the global orchestrator autonomously evaluates metadata tags, applies resource policies, and determines optimal data center selections without manual intervention. This self-automating mechanism improves allocation efficiency while maintaining manageable complexity through rule-based decision-making rather than complex human coordination
3Productivity
If multidimensional metadata tag sets are used for resource selection, then resource placement optimization is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent segments the resource selection process into distinct evaluable components by using separate metadata tags for different criteria (location, latency, resource type). Each tag represents a discrete attribute that can be independently evaluated and matched against policy requirements, making the multidimensional selection process more manageable through modular tag-based filtering rather than monolithic complex queries
Data Source
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AI summary
An example method includes receiving a resource request for at least one compute and/or storage resource from a distributed computing system distributed among multiple data centers, determining a resource policy that is associated with the resource request, wherein the resource policy includes a rule specifying at least one metadata tag and at least one criterion associated with the at least one metadata tag, identifying at least one object included in a resource object model that complies with the rule of the resource policy, wherein the at least one object has an assigned value for the metadata tag that satisfies the at least one criterion, selecting a data center that is associated with the at least one object identified from the resource object model, and deploying, on the selected data center, the at least one compute or storage resource.