IIoT Data Center Service Splitting for Cross-Platform Load Balancing
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Solution Overview
Problem
Industrial Internet of Things (IIoT) data centers face imbalances in computing resource utilization, with some sub-platforms lacking resources while others have idle capacity, necessitating a system for efficient resource allocation across sub-platforms.
Innovation Solution
A management platform splits services across multiple data sub-platforms based on operational and service features, determining resource demands and generating instructions for optimal allocation to balance computational loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If computing resources are allocated independently to each data sub-platform, then each sub-platform can operate autonomously, but computing resource imbalance occurs between sub-platforms
Solution Approach 1:
The patent introduces a management platform that merges the resource allocation decisions of multiple independent data sub-platforms into a unified coordination system. This platform collects operational features from all sub-platforms and makes centralized scheduling decisions to balance computing resource allocation across the entire IIoT data center, resolving the contradiction between autonomous operation and resource balance.
Solution Approach 2:
The management platform continuously collects operational features from data sub-platforms and uses this feedback information to dynamically adjust service scheduling decisions. By monitoring resource utilization patterns and receiving real-time operational data, the system can adaptively redistribute services to maintain computational balance while preserving sub-platform autonomy.
2Productivity
If services are consolidated on fewer sub-platforms, then resource utilization efficiency improves, but system complexity increases due to service splitting and scheduling
Solution Approach 1:
The patent segments services into multiple sub-services that can be independently scheduled across different data sub-platforms. This segmentation enables fine-grained resource allocation, allowing the system to distribute computational workloads more efficiently while the management platform handles the complexity of coordinating these segmented services across the distributed architecture.
3Power
If more data sub-platforms are deployed, then computing capacity increases, but resource imbalance and idle capacity problems worsen
Solution Approach 1:
The management platform implements dynamic service scheduling that adapts to changing operational conditions across multiple data sub-platforms. The system continuously monitors operational features and dynamically adjusts service distribution in real-time, enabling the system to utilize computing capacity efficiently across a large number of sub-platforms while preventing resource imbalance and idle capacity.
Data Source
AI summary
Provided are a method, a system, and a storage medium for service adjustment based on an IIoT data center. The system includes a management platform, the IIoT data center, and a plurality of data sub-platforms in communication with each other. The management platform is configured to: obtain operational features of the plurality of data sub-platforms; determine a first sub-platform and one or more second sub-platforms based on the operational features; determine at least one service to be split, and split the at least one service to be split into a plurality of sub-services; determine processing resource demands corresponding to the sub-services; determine a service adjustment parameter; and generate a service adjustment instruction based on the service adjustment parameter, and send the service adjustment instruction to the first sub-platform and the one or more second sub-platforms, so as to allocate the plurality of sub-services to the one or more second sub-platforms.