Server Cluster Resource Placement Using Constraint Metadata
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Solution Overview
Problem
The process of determining resource placement in a cloud environment is complicated and error-prone, often requiring extensive testing and modification of source code, and there is a lack of efficient methods to manage and deploy resources without disrupting existing systems.
Innovation Solution
A system that utilizes constraints as metadata to manage, identify, and filter resources, allowing for efficient deployment by checking resource creation requests against candidate parent resources' constraints and deploying to those that meet the defined criteria, thereby reducing operational burden and improving flexibility and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If shared resources are used for application deployment, then resource utilization and flexibility are improved, but the complexity and error-proneness of determining resource placement increases
Solution Approach 1:
The patent segments the resource placement determination process into distinct components: constraint definition, constraint validation, and placement execution. By breaking down the complex evaluation process into manageable segments with clear interfaces, the system reduces overall complexity while maintaining flexibility in resource utilization.
Solution Approach 2:
The patent implements preliminary action by defining and validating constraints before actual resource placement occurs. The system pre-establishes placement criteria, validates resource compatibility against these constraints beforehand, and only then proceeds with deployment. This preliminary validation approach simplifies the actual placement process and reduces errors.
2Reliability
If constraint-based resource placement is implemented, then deployment reliability is improved, but the time and computational resources required for evaluation increase
Solution Approach 1:
The patent applies partial action by implementing constraint validation only where necessary - specifically validating against placement constraints before deployment. Rather than performing exhaustive evaluations of all possible placement scenarios, the system focuses validation efforts on critical constraint checks, thereby maintaining reliability while reducing evaluation time and computational overhead.
Solution Approach 2:
The system implements self-service through automated constraint validation and placement determination. The deployment system automatically evaluates resources against defined constraints and makes placement decisions without requiring extensive manual testing or intervention. This automation maintains high reliability while significantly reducing the time investment required compared to manual processes.
3Ease of operation
If dedicated resources are assigned to applications, then deployment simplicity is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements universality by creating a constraint-based framework that can handle both dedicated and shared resource scenarios through a unified approach. The same constraint validation mechanism works for both dedicated resource assignment and shared resource evaluation, simplifying the deployment process while enabling efficient resource utilization across different deployment models.
Solution Approach 2:
The system utilizes parameter changes by allowing constraint definitions to be adjusted based on deployment requirements. Constraints can be modified to reflect different resource allocation strategies (dedicated vs. shared), enabling the system to optimize resource utilization efficiency while maintaining deployment simplicity through parameter adjustment rather than structural changes.
Data Source
AI summary
When a resource is being deployed to the cloud, there are many possible locations for the deployment. When a resource is created, it is placed under a parent resource. As discussed herein, a system for applying constraints to resources is used. A constraint is a type of metadata that helps manage, identify, organize, and filter resources. Defined constraints are a set of constraints that, for a particular resource, are either set or not set. Custom constraints are key/value pairs that have predefined keys, but variable values. New constraints may be added to the system without modifying source code. When a resource creation request is received, constraints of the resource being created are checked against the constraints of the candidate parent resources. The resource is then deployed to a parent resource for which it does not violate any constraints.