Cognitive Recommendation for Cloud Resource Configuration
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Solution Overview
Problem
Users face challenges in determining the optimal configuration for cloud computing resources due to the complexity of hardware and software options, often resulting in undersized or oversized platforms.
Innovation Solution
A cognitive recommendation system that uses a recursive neural network (RNN) and generative adversarial network (GAN) to parse input datasets, predict question sequences, and generate customized recommendations for computing environment attributes, ensuring accurate asset configuration based on user inputs and usage parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually configure cloud computing resources, then they can control the configuration process, but the complexity of hardware and software options makes it difficult to determine optimal configuration
Solution Approach 1:
The system performs self-service by automatically analyzing usage parameters and generating optimized configuration recommendations without requiring user expertise in hardware and software options. The cognitive processing engine autonomously parses input datasets, identifies patterns, and produces asset configuration recommendations, eliminating the need for users to navigate complex configuration options manually.
Solution Approach 2:
The patent introduces a cognitive processing engine as an intermediary between user requirements and cloud resource configuration. This intermediary component parses usage parameters, processes them through machine learning models, and translates them into optimized configuration recommendations, thereby simplifying the interaction between users and complex configuration options.
2Productivity
If users select computing resources without expert guidance, then the process is simple and quick, but the resulting platform may be undersized or oversized
Solution Approach 1:
The system performs preliminary action by pre-processing usage parameters and pre-generating configuration recommendations based on analyzed patterns. The cognitive processing engine prepares optimized configuration options in advance by parsing input datasets and applying machine learning models, so that when users need recommendations, they receive pre-computed accurate suggestions rather than requiring time-consuming manual analysis.
Solution Approach 2:
The system implements feedback by using machine learning models that learn from patterns in usage parameters to continuously improve configuration recommendations. The cognitive processing engine analyzes the relationship between usage parameters and optimal configurations, providing feedback loops that enhance the precision of recommendations over time while maintaining quick response times.
3Adaptability or versatility
If a traditional recommendation system is used, then the implementation is straightforward, but it cannot handle the complexity and ambiguity of usage parameters effectively
Solution Approach 1:
The patent replaces traditional mechanical recommendation systems with cognitive processing based on machine learning. Instead of using rule-based or static algorithms, the system employs machine learning models that can dynamically interpret and adapt to complex and ambiguous usage parameters, thereby enhancing adaptability while managing system complexity through intelligent processing.
Solution Approach 2:
The system applies parameter changes by transforming raw usage parameters into processed features that capture complex patterns and relationships. The cognitive processing engine modifies and transforms input parameters through machine learning preprocessing, enabling the system to handle ambiguity and complexity in usage parameters effectively while generating accurate configuration recommendations.
Data Source
AI summary
An embodiment includes parsing an input dataset associated with a first node of a decision tree, where the input dataset includes a set of profile values for a set of projected usage parameters for a computing environment. The embodiment identifies a structure of the dataset using a recursive neural network that predicts a question sequence in a hierarchical tree format. The embodiment calculates a first deviation from the predicted question sequence and determines whether the deviation exceeds a threshold value. The embodiment generates a modified input dataset using a disambiguation rule and calculates a second deviation of the modified structure from the predicted question sequence and determines whether the deviation exceeds the threshold value. The embodiment assembles a customized hierarchical path using a generative model and assembles the customized hierarchical path by performing iterations of generating a series of candidate questions until a leaf node is reached.