Factor Graph Composition for Cloud Resource Allocation
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Solution Overview
Problem
Existing tools are limited in scaling resource demand forecasting for entire cloud applications, failing to effectively combine sub-graphs from distinct sources into coherent and conflict-free factor graphs, which hampers efficient resource allocation in cloud environments.
Innovation Solution
A method involving the composition of factor graphs, where processors access and unify nodes from multiple factor graphs to create a composite graph, allowing for the selection and deployment of cloud applications to optimal computing resources based on resource needs, including data center and server allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sub-graphs from distinct sources are combined into a single factor graph, then the representation of the entire cloud application is improved, but the complexity of combining and unifying nodes increases
Solution Approach 1:
The patent segments the cloud application into multiple components, each represented by a separate factor graph or sub-graph. This allows independent analysis and modeling of each component's resource demands while maintaining the ability to compose them together. The segmentation principle enables manageable complexity by breaking down the overall system into smaller, independently analyzable units that can be systematically combined.
Solution Approach 2:
The patent employs merging operations to combine multiple sub-graphs into a unified factor graph representing the entire cloud application. The composition operations systematically integrate nodes and factors from different sub-graphs, unifying them while preserving the causal relationships and resource demand information from each component. This merging process resolves the technical contradiction by providing a systematic method to combine sub-graphs without overwhelming complexity.
2Productivity
If factor graphs from distinct sources are composed together, then comprehensive resource allocation is achieved, but conflicts between sub-graphs become difficult to resolve
Solution Approach 1:
The patent applies preliminary action by performing composition operations in a systematic sequence, building the unified factor graph incrementally from individual sub-graphs. The composition process prepares and validates each integration step beforehand, identifying and resolving conflicts between sub-graphs as they arise during the composition process rather than attempting to resolve all conflicts simultaneously. This staged approach makes the operation manageable and systematic.
Solution Approach 2:
The patent uses composition operations as intermediary mechanisms that mediate between conflicting sub-graphs. These operations serve as systematic intermediaries that translate and integrate nodes and factors from different sources, resolving conflicts through defined composition rules. The intermediary composition process transforms multiple conflicting representations into a unified, conflict-free factor graph that captures the comprehensive resource allocation needs.
3Measurement precision
If a unified factor graph is created from multiple sources, then accurate resource demand forecasting is achieved, but the computational complexity increases
Solution Approach 1:
The patent segments the computational task of resource demand forecasting into multiple smaller sub-tasks, each corresponding to a component factor graph. By maintaining segmented sub-graphs that can be independently analyzed and composed, the system achieves accurate forecasting for the entire application without requiring computationally expensive analysis of all nodes simultaneously. The segmentation enables efficient computation while preserving forecasting precision.
Solution Approach 2:
The patent applies partial action by composing factor graphs incrementally and selectively, rather than attempting to process all nodes and factors simultaneously. The composition operations build the unified graph in stages, processing only the necessary portions at each step. This approach achieves accurate resource demand forecasting through systematic partial composition, reducing the overall computational burden while maintaining precision.
Data Source
AI summary
A computer-implemented method of deploying a cloud application comprises accessing, by one or more processors, a first factor graph that represents a first component of the cloud application, the first factor graph comprising a first set of nodes; accessing a second factor graph that represents a second component of the cloud application, the second factor graph comprising a second set of nodes; determining a third set of nodes that are present in both the first set of nodes and the second set of nodes; joining, by the one or more processors, the first factor graph and the second factor graph into a third factor graph, wherein the joining includes unifying the third set of nodes in the first factor graph and the second factor graph; based on the third factor graph, selecting computing resources; and deploying at least a portion of the cloud application to the selected computing resources.