Cloud Resource Management via Neural Network Event Prediction
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Solution Overview
Problem
Current methods for managing resources in cloud environments are inefficient and require human intervention, leading to errors and increased resource usage, as they lack intelligence and automation for tasks such as error resolution and network monitoring.
Innovation Solution
A method and system that utilize a neural network trained on parameters from the cloud environment to predict and manage events, converting parameter values into vectors and storing outputs for future reference, enabling automated decision-making and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional methods with pre-defined rules are used for resource management, then automation is partially achieved, but error rate increases and reliability decreases
Solution Approach 1:
The system employs self-healing capabilities where the cloud environment automatically detects, diagnoses, and resolves issues without human intervention. The error detection module continuously monitors system state and triggers automated recovery procedures when anomalies are detected, enabling the system to service itself and maintain high reliability through autonomous operation.
Solution Approach 2:
The system implements continuous feedback loops through monitoring modules that track system performance and error conditions. This feedback is processed by machine learning models that adapt their behavior based on observed patterns, allowing the system to learn from past errors and improve its response strategies, thereby reducing error rates while maintaining automation.
2Reliability
If human intervention is increased for error resolution and network monitoring, then reliability improves, but resource consumption and operational complexity increase
Solution Approach 1:
The system replaces human operators with automated error detection and resolution modules that continuously monitor cloud infrastructure. These modules automatically diagnose issues, execute recovery procedures, and adapt to new error patterns through machine learning, eliminating the need for human intervention while maintaining or improving reliability and reducing operational complexity.
Solution Approach 2:
The patent substitutes mechanical human operations with intelligent automated systems. Human experts previously performed error analysis and resolution manually; this function is replaced by software-based error detection algorithms and machine learning models that process data, identify patterns, and execute corrections automatically, thereby reducing system complexity from a human-dependent structure to an autonomous software system.
3Reliability
If continuous network monitoring is implemented, then security and reliability improve, but resource consumption increases
Solution Approach 1:
The system implements selective monitoring that focuses computational resources on critical network traffic and security-relevant events rather than continuously analyzing all data flows. The error detection module activates monitoring at specific thresholds or when anomalies are detected, reducing overall resource consumption while maintaining security and reliability through targeted observation of key parameters.
Solution Approach 2:
The continuous monitoring function is implemented through periodic sampling and event-triggered detection rather than constant full-scale analysis. The system checks network parameters at regular intervals and intensifies monitoring only when specific conditions are met, such as unusual traffic patterns or security threats, thereby maintaining high reliability while significantly reducing resource consumption during normal operation.
Data Source
AI summary
A method of managing resources in a cloud environment is disclosed. The method includes receiving a plurality of parameters associated with an event. The method further includes comparing a value of each of the plurality of parameters with a predefined threshold range. The method includes converting the value of each of the plurality of parameters into a vector, when the value of each of the plurality of parameters is within the predefined threshold range. The method further includes training a neural network based on the vector of the value of each of the plurality of parameters, wherein the neural network is trained to manage the event. The method includes storing an output of the trained neural network in a database in response to the training. The output corresponds to management of the event and the database further comprises a mapping of the event to the trained neural network.