Rack Power Allocation Using Telemetry to Prevent Brownouts
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Solution Overview
Problem
Existing data processing systems face challenges in managing power consumption effectively, leading to potential brownouts and inefficiencies due to unregulated power demands.
Innovation Solution
A system and method for managing power consumption by dynamically allocating power based on telemetry data, using power allocation manager to compute and enforce power limits for data processing systems and racks, incorporating dynamic weighted priorities and rack power limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If power consumption is increased to improve computational performance, then productivity is improved, but reliability deteriorates due to potential brownouts
Solution Approach 1:
The patent implements dynamic power allocation that continuously adjusts power limits based on real-time telemetry data, workload conditions, and system state. Power limits are not static but adapt dynamically to balance computational performance with system stability, preventing brownouts while maximizing productivity when conditions allow.
Solution Approach 2:
The system employs feedback mechanisms by monitoring telemetry data from data processing systems and using this information to adjust power allocation decisions. The power allocation manager receives feedback on system performance and power consumption, then modifies power limits accordingly to maintain both high productivity and system reliability.
2Reliability
If power limits are enforced to prevent brownouts, then reliability is improved, but productivity deteriorates due to restricted power usage
Solution Approach 1:
Power limits are implemented as dynamic rather than static constraints. The system continuously monitors system state and telemetry data, adjusting power limits in real-time to prevent brownouts while allowing maximum computational performance when power availability and system conditions permit.
Solution Approach 2:
The system changes power allocation parameters based on monitored conditions. By adjusting power limits dynamically according to telemetry data, workload characteristics, and system state, the system maintains reliability through enforced limits while optimizing productivity when conditions allow higher power consumption.
3Device complexity
If static power allocation is used to simplify management, then device complexity is reduced, but adaptability deteriorates due to inability to respond to changing conditions
Solution Approach 1:
The power allocation manager implements self-service by autonomously monitoring telemetry data and adjusting power allocation without requiring manual intervention. The system automatically adapts to changing conditions based on real-time data from the data processing systems, maintaining low operational complexity while achieving high adaptability.
Solution Approach 2:
The system dynamically changes power allocation parameters based on monitored conditions and telemetry data. This automated parameter adjustment enables the system to adapt to changing workloads and system states without increasing operational complexity, as the changes are driven by automated monitoring and decision-making algorithms.
4Adaptability or versatility
If dynamic power allocation is implemented to improve adaptability, then adaptability is improved, but device complexity increases due to monitoring and computation requirements
Solution Approach 1:
The power allocation system is segmented into distinct functional components: telemetry data collection, weight calculation, power limit determination, and enforcement mechanisms. This segmentation allows each component to perform its specific function efficiently, managing complexity through modular architecture while maintaining high adaptability through coordinated operation of the segments.
Solution Approach 2:
The power allocation manager acts as an intermediary between the data processing systems and power supply infrastructure. It collects telemetry data, processes this information through weight calculations, determines appropriate power limits, and enforces these limits. This intermediary layer manages the complexity of dynamic power allocation while providing simplified interfaces to both the data processing systems and power infrastructure.
Data Source
AI summary
Methods and systems for managing power consumption in data processing systems are disclosed. The power consumption in the data processing systems may be managed by obtaining power limits for the data processing systems. The power limits may be determined by computing the power limits from regularly updated parameters of the data processing systems. The data processing systems may include data processing systems that are housed in racks. From the regularly updated parameters, rack power limits may be obtained. Using the regular parameters and the rack power limits, data processing system power limits may be obtained for the data processing systems.