Dynamic Computing Allocation in IIoT Data Centers
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Solution Overview
Problem
The challenge of dynamically allocating computing resources in an industrial Internet of Things (IIoT) data center to meet varying business demands and optimize production line operations is not adequately addressed by existing technologies.
Innovation Solution
A system and method for dynamic computing resource allocation in an IIoT data center, comprising an IIoT user platform, service platform, management platform, sensing network platform, and sensing control platform, which includes a control center that determines resource allocation parameters using machine learning models to optimize resource distribution based on business demands and production status data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are allocated statically in an IIoT data center, then system simplicity is maintained, but resource utilization efficiency deteriorates under varying business demands
Solution Approach 1:
The patent implements dynamic resource allocation by introducing a resource allocation management module that continuously monitors resource usage metrics and business demand patterns, automatically adjusting computing resource distribution in real-time based on current system state, thereby resolving the contradiction between system simplicity and resource utilization efficiency
Solution Approach 2:
The system establishes a feedback mechanism where resource allocation decisions are continuously optimized based on monitored performance metrics and business demand data, allowing the system to adapt to varying conditions while maintaining operational simplicity through automated control loops
2Reliability
If computing resources are allocated to meet peak business demands, then service quality is improved, but resource waste increases during low-demand periods
Solution Approach 1:
The patent applies dynamic allocation strategies that adjust computing resource provisioning based on real-time demand assessment, scaling resources up during peak periods to maintain service quality and scaling down during low-demand periods to reduce energy consumption and resource waste
Solution Approach 2:
The system changes resource allocation parameters dynamically based on business demand characteristics, adjusting CPU, memory, and storage allocation ratios according to the specific requirements and intensity of different business workloads, thereby optimizing both service quality and resource efficiency
3Productivity
If computing resources are allocated uniformly across all business scenarios, then allocation simplicity is maintained, but business-specific performance optimization deteriorates
Solution Approach 1:
The patent implements local quality optimization by allocating computing resources according to the specific characteristics of different business scenarios, providing customized resource configurations for different workloads such as data processing, analytics, and real-time control, thereby enhancing business-specific performance
Solution Approach 2:
The system segments the resource allocation strategy into different allocation policies for different business types and workload characteristics, allowing each segment to receive optimized resource allocation tailored to its specific performance requirements while maintaining overall system coordination
Data Source
AI summary
Embodiments of the present disclosure provide a method for dynamic computing resource allocation based on an IIoT data center, the method comprising: receiving, via an IIoT user platform 110, a business demand of an enterprise user, and sending the business demand via an IIoT service platform 120 to a data computing center of a IIoT management platform 130; monitoring, by a monitoring module, resource data of a business management sub-platform; determining, based on the business demand, a resource demand feature of the enterprise user; determining, based on the resource demand feature and the resource data of the business management sub-platform, a resource allocation parameter; and, generating, via the control center, a resource allocation instruction based on the resource allocation parameter.