Neural Network Workload Classification via NLP Signatures
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Solution Overview
Problem
Current methods for classifying workloads in IT infrastructures rely on manual keyword-based searches, which are inefficient and require frequent updates, limiting visibility into actual workloads and server performance metrics, and often fail to provide real-time insights for sales teams and product development.
Innovation Solution
A system utilizing natural language processing (NLP) and neural network models, such as Doc2vec, to automatically classify workloads by generating workload signatures from data sources like CRM and product logs, reducing human intervention and enabling accurate, real-time identification of workload types within IT infrastructures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual keyword-based search methods are used to classify workloads, then implementation simplicity is maintained, but classification efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces manual keyword-based search (mechanical/manual system) with an automated neural network model (intelligent system). The neural network automatically learns workload patterns from data sources like CRM and product logs, eliminating the need for manual keyword searching and significantly improving classification efficiency and accuracy while maintaining ease of implementation through automated processes.
2Device complexity
If manual keyword-based search methods are used to classify workloads, then system complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent substitutes manual keyword matching with a neural network model that automatically learns and identifies workload patterns. This intelligent system analyzes multiple data sources simultaneously and generates accurate workload classifications without requiring complex manual configuration, thereby improving measurement precision while keeping the system manageable through automated learning processes.
3Ease of operation
If manual workload classification is performed, then real-time processing capability is reduced, but operational simplicity is maintained
Solution Approach 1:
The patent implements a self-service automated classification system where the neural network model independently processes workload data in real-time without requiring manual intervention. The system automatically ingests data from multiple sources, performs classification, and generates insights, thereby achieving real-time processing capability while maintaining operational simplicity through autonomous operation.
4Measurement precision
If automated neural network models are used to classify workloads, then classification accuracy and real-time insights are improved, but computational resource requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the neural network model offline using historical workload data from CRM and product logs. This pre-training phase computes complex patterns and relationships in advance, allowing the model to perform fast, accurate real-time classifications with minimal computational resources during actual workload analysis, thereby balancing accuracy with resource efficiency.
Data Source
AI summary
A system, method, and computer-readable medium for performing a workload classification and analysis operation. The workload classification and analysis operation includes performing the steps of receiving workload data from a data source; generating a neural network model from the workload data; defining a plurality of workload signatures, the plurality of workload signatures defining a particular type of workload; identifying particular workloads using the plurality of workload signatures; and, providing information regarding the particular workloads to a user.