Edge Server Chassis Manager Using Predictive Temperature Control
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Solution Overview
Problem
Current edge server management technologies focus solely on hardware reinforcement for rugged environments, failing to address the challenges posed by severe climate conditions, and lack compatibility with industrial standards.
Innovation Solution
An adaptive temperature control method for edge servers using log analysis, predicting future workloads and temperatures, and controlling fan and heater modules, as well as work transfers, to manage and protect the server in rugged conditions while adhering to Redfish standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If hardware reinforcement is used to protect edge servers in rugged environments, then device strength is improved, but adaptability to varying climate conditions deteriorates
Solution Approach 1:
The system performs preliminary analysis of work logs to predict future workload and temperature conditions before they actually occur. This allows the edge server to proactively adjust cooling/heating operations and workload distribution in advance, rather than merely reacting to hardware stress after climate conditions deteriorate.
Solution Approach 2:
The system continuously monitors work logs, predicts future temperatures, and adjusts cooling/heating operations based on this feedback loop. This closed-loop control enables the system to adapt dynamically to varying climate conditions while maintaining operational strength through predictive rather than static hardware protection.
2Temperature
If cooling operations are increased to manage high temperature, then temperature control is improved, but energy consumption increases
Solution Approach 1:
The system predicts future temperature and workload conditions before they occur, allowing cooling operations to be adjusted in advance. This prevents excessive cooling when not needed and optimizes energy usage by only activating cooling operations when actually required based on predictive analysis.
Solution Approach 2:
The system applies cooling operations selectively based on predicted temperature conditions rather than continuously. By using partial action (cooling only when necessary) guided by predictive analysis, the system achieves adequate temperature control while minimizing unnecessary energy consumption.
3Temperature
If workload is transferred to another edge server to reduce temperature, then temperature control is improved, but system complexity increases
Solution Approach 1:
The system uses feedback from temperature predictions and workload analysis to decide when workload transfer is necessary. This intelligent decision-making process reduces unnecessary transfers and optimizes the complexity-benefit ratio by only activating workload transfer when predictive analysis indicates it will effectively resolve temperature issues.
Solution Approach 2:
The system changes operational parameters (workload distribution, cooling intensity) based on predicted temperature conditions. By dynamically adjusting these parameters rather than using fixed configurations, the system manages temperature effectively while adapting complexity to actual needs rather than maintaining constant high complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables effective temperature management and work distribution in rugged environments, ensuring continuous edge service operation and compliance with industrial standards.
Implementation Method 1
a fan module configured to reduce an internal temperature of the edge server system
Implementation Method 2
a heater module configured to increase the internal temperature of the edge server system
Data Source
AI summary
There is provided an adaptive temperature control method based on log analysis of a chassis manager in an edge server. The adaptive temperature control method of the edge server system according to an embodiment includes: collecting, by a chassis manger module of the edge server system, work logs of a computing module and a storage module; predicting a future work load from the collected work logs; predicting a future internal temperature of the edge server system, based on the work load and a future temperature; and controlling, by the chassis manager module, the edge server system, based on the predicted future internal temperature. Accordingly, a configuration module of an edge server system may be managed/controlled in a rugged environment, and temperature of the edge server system may be adaptively controlled by transferring or additionally generating works of an edge server.