Capacity-Weighted Graph for Cloud Resource Prediction
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Solution Overview
Problem
Managing resource capacity in private cloud environments is challenging due to unpredictable resource needs, as existing methods are not cost-effective or practical for anticipating capacity requirements for individual services, especially in shared resource scenarios.
Innovation Solution
A method and system that utilize a capacity-weighted graph to monitor application deployments, resource dependencies, and service consumption to predict and manage resource capacity needs, allowing for dynamic adjustment and hybrid cloud integration when private cloud resources are exhausted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large-scale analytics and prediction models are implemented to predetermine resource needs, then resource availability is improved, but cost and complexity increase significantly
Solution Approach 1:
The patent segments the resource capacity management problem by creating a capacity-weighted graph that breaks down the system into individual service components and their dependencies. Each service is analyzed separately for its capacity requirements, and the graph structure allows incremental construction and updating of the model, avoiding the need for complex monolithic prediction models while maintaining resource availability.
2Measurement precision
If capacity-weighted graph and monitoring operations are implemented, then resource capacity prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic updating of the capacity-weighted graph through monitoring operations that continuously track service consumption and deployment changes. The graph is updated incrementally as new information becomes available, allowing the system to adapt to changing resource requirements without requiring complete model reconstruction, thereby improving prediction accuracy while managing system complexity.
3Adaptability or versatility
If shared resources are not committed to specific services, then resource flexibility and sharing efficiency are improved, but ability to anticipate capacity requirements deteriorates
Solution Approach 1:
The patent employs feedback mechanisms through monitoring operations that track actual service consumption against predicted requirements. This feedback loop allows the system to learn from actual usage patterns and refine capacity predictions for shared resources, maintaining both resource flexibility and the ability to anticipate capacity requirements accurately through continuous observation and model updating.
Data Source
AI summary
A method, system and computer-usable medium are disclosed for managing resource capacity for the provision of cloud-based services. Application deployment and undeployment data is received, and then processed, to perform an update of a capacity-weighted graph to reflect a set of applications deployed in a cloud computing environment. The application deployment and undeployment data is then further processed to determine a set of resource dependencies associated with the set of deployed applications. Thereafter, monitoring operations are performed to monitor consumption of a set of services associated with the set of deployed applications. Further monitoring operations are then performed to monitor requests from the set of deployed applications to predict the need for the provision of a set of additional services for consumption at runtime.