Attribute Dependency Graph for Computing Resource Allocation
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Solution Overview
Problem
Existing computing environments face challenges in optimizing resource allocation and predicting performance issues, leading to inefficiencies, bottlenecks, and potential system crashes.
Innovation Solution
A performance assurance framework that uses an attributes dependency graph to compute resource allocation and configurations in a computing environment, based on available resources and constraints, while also employing machine learning to determine edge functions and predict future issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used, then device complexity is reduced, but productivity and reliability deteriorate due to inefficient resource utilization and inability to predict performance issues
Solution Approach 1:
The system segments the complex resource allocation problem into multiple manageable components: attribute dependency graphs break down performance relationships into discrete attribute-node connections, factor graphs separate constraints into independent factors, and the optimization process divides resource allocation into iterative steps of graph generation, constraint propagation, and solution refinement. This segmentation allows complex computing environments to be analyzed and optimized systematically without overwhelming complexity.
Solution Approach 2:
The patent introduces intermediate representations (attribute dependency graphs and factor graphs) as mediators between the complex computing environment and the optimization process. These graphs translate complex resource relationships into structured formats that can be processed algorithmically, enabling automated optimization while maintaining clarity in the system architecture. The graphs serve as intermediary structures that bridge the gap between environmental complexity and computational tractability.
2Reliability
If resource allocation is optimized without prediction capabilities, then device complexity is reduced, but reliability worsens due to inability to predict and mitigate future performance issues
Solution Approach 1:
The system performs preliminary actions by generating attribute dependency graphs and factor graphs before actual resource allocation occurs. These graphs capture performance relationships and constraints in advance, enabling the system to predict future performance issues and allocate resources proactively. The preliminary graph generation and constraint propagation steps prepare the optimization system to anticipate and prevent performance degradation before it occurs.
Solution Approach 2:
The optimization framework incorporates feedback mechanisms where performance attributes and constraints are continuously monitored, and this information feeds back into the graph generation and optimization processes. The system uses actual performance data to refine attribute dependency graphs and adjust resource allocation decisions, creating a closed-loop system that learns from past performance and improves future predictions and allocations.
3Measurement precision
If machine learning is employed to determine edge functions, then measurement precision and prediction accuracy improve, but device complexity and computational requirements increase
Solution Approach 1:
The system changes parameters by using machine learning models to determine edge functions in the attribute dependency graphs. Instead of using fixed, simple relationships between attributes, the system employs learned functions that adapt to actual performance patterns. This allows the system to capture non-linear and complex performance relationships that would be difficult to encode manually, improving prediction accuracy while the ML models themselves handle the computational complexity.
Data Source
AI summary
A system, method, and a computer program product for allocating and configuring resources to an application in a computing environment are provided. An attributes dependency graph is generated from an attributes dependency model defining a plurality of attributes. The plurality of attributes correspond to a plurality of resources and performance metrics in a computing environment. The vertices in the attributes dependency graph correspond to the attributes. The edges in the attributes dependency graph correspond to functions identifying relationships between at least two vertices. A factor graph is generated from the attributes dependency graph and a constraints model. The constraint model specifies at least one constraint for an application executing in the computing environment. A subset of resources in the computing environment is allocated or configured to the application based on the factor graph.