Cooperative control optimization method suitable for near-field fan wall micromodule
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Solution Overview
Problem
The lack of effective coordination between IT system and cooling system scheduling in near-field fan wall air conditioners leads to issues like local hot spots and overcooling, resulting in inverse fluctuations in energy consumption and stability problems in data centers.
Innovation Solution
A cooperative control optimization method that combines real-time joint optimization and timed decoupling optimization, using models for server power and temperature prediction to dynamically match heat dissipation and cooling supply, involving server deployment and air-conditioning system adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate scheduling is used for IT system and cooling system, then each system can be controlled independently, but local hot spots and overcooling occur due to lack of coordination
Solution Approach 1:
The patent merges the IT system scheduling and cooling system control into a unified cooperative control framework. The joint optimization model simultaneously considers IT workload deployment and cooling system parameters (fan speed, coil temperature), enabling coordinated decision-making that prevents local hot spots and overcooling while maintaining operational flexibility.
Solution Approach 2:
The patent introduces a cooperative control optimization model as an intermediary layer between the IT system and cooling system. This model uses joint optimization algorithms to mediate the interaction between server deployment and cooling parameter adjustment, ensuring temperature uniformity while preserving independent control capabilities through decoupled optimization modes.
2Use of energy by moving object
If IT system prioritizes least active servers for energy saving, then IT system energy consumption decreases, but cooling system energy consumption increases due to uneven heat distribution
Solution Approach 1:
The patent applies preliminary action by predicting future heat dissipation patterns based on current IT workload distribution. The cooperative control model uses prediction models to anticipate temperature changes and proactively adjusts cooling parameters before hot spots develop, enabling the IT system to optimize energy consumption without causing excessive cooling energy demand.
Solution Approach 2:
The patent implements feedback mechanisms where temperature sensors continuously monitor server temperatures and cooling system performance. This real-time feedback is fed into the joint optimization model, which dynamically adjusts both IT workload deployment and cooling parameters to balance energy consumption between the two systems while preventing temperature extremes.
3Reliability
If cooling system uses feedback control based on supply and return air temperatures, then cooling response is achieved, but time lag and regulation precision issues occur due to fluid distribution characteristics
Solution Approach 1:
The patent uses prediction models to estimate future server temperatures based on current workload and cooling parameters. This preliminary temperature estimation allows the cooperative control system to proactively adjust cooling parameters before actual temperature changes occur, significantly reducing the time lag inherent in traditional feedback control systems that wait for temperature sensors to detect changes.
Solution Approach 2:
The patent implements dynamic control by continuously updating the joint optimization model with real-time data from both IT and cooling systems. The model dynamically adjusts cooling parameters based on predicted temperature trends and actual system state, enabling rapid response to workload changes without the inertia and time lag characteristic of static feedback control systems.
Data Source
AI summary
A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps: step 10: obtaining IT system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model. The present invention provides a cooperative control optimization method suitable for a near-field fan wall micromodule, to resolve the technical problem that local hot spots and overcooling are prone to occur during energy-saving scheduling of a near-field fan wall micromodule.