PCIe Power Budget Table Correction Using Telemetry Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual entry of Power Budget Table (PBT) values for PCIe cards in information handling systems is error-prone, leading to incorrect thermal and power management, which can cause performance degradation, failures, and increased support costs.
Innovation Solution
A framework that proactively assesses and corrects PBT values using telemetry data, classification algorithms, and conformal prediction to recommend accurate PBT values, applying them dynamically through IPMI commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual entry of PBT values is used, then flexibility in configuration is improved, but accuracy and reliability deteriorate due to human error
Solution Approach 1:
The system automatically retrieves PBT values from specification sheets and databases without requiring manual entry. The BMC autonomously configures power budget parameters by querying manufacturer databases using device identifiers, eliminating human error while maintaining configuration flexibility through automated adaptation to different PCIe devices.
Solution Approach 2:
The manual mechanical process of entering PBT values is replaced with an automated electronic system. The BMC uses device identifiers to automatically query databases and retrieve appropriate PBT values, substituting human operation with electronic automation that enhances both accuracy and efficiency.
2Stability of the object's composition
If PBT modifications are implemented only during BMC firmware updates, then system stability is improved, but responsiveness to corrections deteriorates
Solution Approach 1:
The system transitions from static PBT configuration (only changed during firmware updates) to dynamic configuration. The BMC can modify PBT values at runtime by querying databases and applying corrections immediately, allowing the system to adapt to newly discovered errors without requiring firmware updates, thus balancing stability with responsiveness.
Solution Approach 2:
The system performs preliminary validation and correction of PBT values before they are applied. The BMC queries specification sheets and databases in advance to verify PBT parameters, and can prepare corrections that are then applied immediately when errors are detected, rather than waiting for scheduled firmware updates.
3Productivity
If incorrect PBT values are used, then configuration speed is improved, but system performance and availability deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the BMC continuously monitors PCIe device performance and thermal conditions. When deviations from expected behavior are detected, the system queries the database to verify PBT values and automatically applies corrections, creating a closed-loop system that maintains both speed and reliability through continuous validation.
Solution Approach 2:
The system performs preliminary validation of PBT values against specification sheets and database records before applying configurations. This pre-validation ensures that only accurate values are used, preventing performance degradation while maintaining configuration speed through automated verification processes.
Data Source
AI summary
Disclosed systems and methods determine a current configuration of an information handling system and execute a suitable classification algorithm to classify configurations of other information handling systems as either matching or not matching the current configuration. The current configuration may be determined based on telemetry data generated by the information handling system. The telemetry data may be uploaded to a backend configuration store. After classifying matching and not-matching configurations, a conformal prediction framework may then be invoked to determine one of the matching configurations as the best matching or closest configuration to the current configuration. Recommended values for one or more configuration features may then be determined based on the closest configuration. The recommend values may then be applied to the one or more configuration features.