Hardware Reuse Matching for Workload-Based Component Life Extension
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Solution Overview
Problem
The challenge is to extend the lifetime of hardware components in information handling systems while minimizing greenhouse gas emissions associated with the manufacture, transport, and disposal of replacement parts. Existing technologies do not effectively address the reuse of underperforming hardware components due to high workload in one system affecting another system of the same hardware type.
Innovation Solution
A machine learning-based hardware reuse recommendation system that analyzes crowd-sourced data from multiple information handling systems to recommend the reuse of underperforming hardware components. The system identifies suitable recipients for these components by matching systems with lower workloads of the same hardware type, while also considering geographic proximity to minimize transportation-related emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If hardware components are reused across information handling systems, then hardware component lifetime is extended and manufacturing emissions are reduced, but the complexity of matching suitable recipients increases
Solution Approach 1:
A machine learning-based intermediary system is introduced to mediate between hardware components needing reuse and potential recipient systems. The sustainability engine acts as a mediator that processes crowd-sourced telemetry data, analyzes compatibility, and generates reuse recommendations, thereby managing the complexity of matching while extending hardware lifetime.
Solution Approach 2:
The system changes parameters by analyzing multiple dimensions including hardware type compatibility, workload characteristics, geographic location, and environmental impact metrics. By transforming these parameters into a standardized recommendation framework, the system manages complexity while optimizing hardware reuse decisions.
2Measurement precision
If crowd-sourced data from multiple systems is analyzed to find suitable reuse recipients, then hardware reuse accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing crowd-sourced telemetry data from multiple information handling systems before reuse requests are made. This advance preparation of data includes organizing hardware inventories, workload profiles, and location information, enabling faster and more accurate matching when reuse decisions are needed.
Solution Approach 2:
The system creates simplified copies or representations of complex system states through standardized data models and telemetry metrics. By working with these condensed representations rather than raw data, the system achieves high matching accuracy while reducing computational overhead and processing time.
3Object-generated harmful factors
If geographic proximity is considered to minimize transportation emissions, then environmental impact is reduced, but the pool of suitable reuse recipients decreases
Solution Approach 1:
The system dynamically adjusts the weight of geographic proximity as a parameter in its recommendation algorithm. By changing this parameter's influence based on hardware type, urgency, and available recipients, the system balances emission reduction with maintaining a sufficient pool of suitable recipients, rather than applying a fixed geographic constraint.
Data Source
AI summary
A method of recommending reuse of hardware components across client information handling systems to extend hardware life may comprise receiving operational telemetries from a first and second information handling system, each including an error associated with a hardware type, and measured workloads on the hardware type, determining, based on the operational telemetries that a hardware component of the same type is failing at both the first and second information handling systems, and that the first information handling system is within a preset maximum distance from the second information handling system, determining failure of the component at the first information handling system is due to a workload on that component that is higher than the workload on the same component at the second information handling system, and transmitting a recommendation to replace the component at the second information handling system with the component from the first information handling system.