Automated Infrastructure Resource Allocation System
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Solution Overview
Problem
Large organizations face challenges in efficiently allocating and provisioning compute, network, and storage resources due to their finite nature and changing requirements, necessitating automated methods for capacity allocation, brokerage, and placement to ensure uninterrupted business operations.
Innovation Solution
A method and system utilizing a processor to receive resource requirement data, analyze availability using a machine learning model trained on historical data, and provision resources accordingly, including generating notifications and updating allocations based on current and projected needs, with the ability to detect and reallocate errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated allocation and provisioning systems are implemented, then resource allocation efficiency and productivity are improved, but device complexity and system complexity increase
Solution Approach 1:
The system enables self-service through automated allocation and provisioning mechanisms where the resource management system automatically analyzes resource requirements, determines optimal allocations, and provisions resources without manual intervention. The machine learning model autonomously makes allocation decisions based on historical data and patterns, reducing the need for human involvement in routine allocation tasks.
Solution Approach 2:
The patent replaces manual mechanical allocation processes with automated computer-based systems using machine learning algorithms. The machine learning model substitutes human decision-making with automated computational analysis, using historical data to predict and determine optimal resource allocations, thereby eliminating manual intervention in the allocation process.
2Device complexity
If manual resource allocation processes are used, then system complexity is lower, but allocation efficiency and responsiveness to changing requirements deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously analyzes historical allocation data and outcomes to improve future allocation decisions. Operational data from executed allocations is fed back into the system to refine the machine learning model's predictions and allocation strategies, creating a continuous improvement loop that enhances allocation efficiency over time.
Solution Approach 2:
The machine learning model performs preliminary analysis of resource requirements and availability patterns before actual allocation occurs. By predicting future resource needs and optimal allocation strategies in advance based on historical data, the system prepares allocation decisions ahead of time, enabling rapid response to changing requirements without manual intervention.
3Speed
If automated provisioning is implemented, then resource allocation speed increases, but measurement precision and monitoring requirements increase
Solution Approach 1:
The system uses feedback from operational data and monitoring information to continuously improve allocation accuracy. Monitoring data about resource usage patterns, allocation outcomes, and system performance is fed back into the machine learning model to refine future predictions and allocation decisions, ensuring increasing precision with automated provisioning.
Solution Approach 2:
Manual monitoring and measurement processes are replaced with automated computer-based monitoring systems that continuously track resource allocation and usage. The machine learning model automatically analyzes monitoring data to measure allocation precision and optimize future allocations, substituting manual measurement with automated computational analysis.
Data Source
AI summary
A method and a system for automated performance of capacity allocation, brokerage, placement, and provisioning of compute, network, and storage resources are provided. The method includes: receiving a first data set that relates to resource requirements of a user; retrieving, from a memory, a second data set that relates to resource availability; analyzing the first data set and the second data set in order to determine a proposed allocation of resources and a proposed timing that corresponds to the proposed allocation; and provisioning the resources to the user based on the proposed allocation and the proposed timing. A machine learning model that is trained by using historical resource allocation data may be applied to the first data set and the second data set in order to perform the analysis.