Policy-Based Cloud Resource Scaling
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Solution Overview
Problem
Current scalability functionality in cloud computing environments does not consider customer-specific policies and constraints when adding or removing computing resources, leading to inefficient resource allocation.
Innovation Solution
Implementing a policy-driven, price-sensitive scaling approach that identifies the need for resource scaling based on workload requests and adjusts computing resources accordingly, while adhering to customer-defined pricing criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current scalability functionality adds or removes resources without considering customer-specific policies, then resource scaling is achieved, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent changes the parameters of resource scaling by introducing policy-based constraints (pricing criteria, resource type preferences, location preferences) that modify how scaling decisions are made. Instead of purely demand-driven scaling, the system now scales according to both demand and customer-defined policy parameters, resolving the contradiction between scaling capability and allocation efficiency.
Solution Approach 2:
The system implements feedback mechanisms where customer policies are continuously referenced during scaling decisions. The scaling system receives feedback from policy evaluations and adjusts resource allocation accordingly, ensuring that scaling actions align with customer requirements while maintaining allocation efficiency.
2Productivity
If computing resources are scaled without considering pricing criteria, then workload processing capability is improved, but cost management deteriorates
Solution Approach 1:
The patent introduces pricing criteria as a new parameter that constrains scaling decisions. The system now evaluates scaling options against pricing thresholds and customer-defined cost parameters, preventing excessive resource provisioning while ensuring adequate workload processing capability. This resolves the contradiction between processing capability and cost management.
Solution Approach 2:
The system applies partial action by scaling resources only to the extent necessary and permitted by pricing criteria. Instead of fully scaling to meet maximum demand, the system scales partially according to policy constraints, optimizing the balance between processing capability and cost management.
Data Source
AI summary
Embodiments of the present invention provide an approach for policy-driven (e.g., price-sensitive) scaling of computing resources in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a workload request for a customer will be received and a set of computing resources available to process the workload request will be identified. It will then be determined whether the set of computing resources are sufficient to process the workload request. If the set of computing resources are under-allocated (or are over-allocated), a resource scaling policy may be accessed. The set of computing resources may then be scaled based on the resource scaling policy, so that the workload request can be efficiently processed while maintaining compliance with the resource scaling policy.