Dynamic Power Service for Server Resource Adaptation
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Solution Overview
Problem
Data centers face challenges in managing power and thermal issues efficiently, particularly during unexpected bursts in demand, which can lead to performance degradation and trigger power protection systems, limiting the ability to optimize server performance across different priority services.
Innovation Solution
A dynamic power service that employs DP agents to monitor and manage power consumption and thermal capacity across data centers, selectively reducing power usage by lower priority services to maintain high performance for higher priority services, and utilizing turbo boost modes in CPUs when safe to do so, while tracking power and thermal limits to avoid triggering protection systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If power is budgeted with configuration limits based on available power to ensure adequate power supply, then power reliability is improved, but system adaptability deteriorates due to inability to respond to unexpected demand bursts
Solution Approach 1:
The system dynamically adjusts power allocation and server configuration limits based on real-time power availability and demand conditions. The power budgeting system transitions from static configuration limits to dynamic adjustment, allowing the system to adapt to unexpected demand bursts while maintaining power supply reliability through continuous monitoring and adjustment of power distribution.
Solution Approach 2:
The system changes power consumption parameters by adjusting server operational states, enabling turbo modes, and modifying configuration limits based on real-time conditions. Power allocation parameters are dynamically modified to balance reliability requirements with adaptability to varying demand conditions, allowing the system to respond to demand bursts without exceeding power capacity.
2Reliability
If circuit breakers are placed in-line with power supply to prevent exceeding power budget, then power protection is improved, but system productivity deteriorates due to power cuts during demand surges
Solution Approach 1:
The system performs preliminary actions by proactively managing power allocation and server states before demand surges occur. Power budgeting decisions are made in advance based on predicted demand patterns, and servers are pre-configured to optimize power usage, allowing the system to handle demand surges without triggering circuit breakers and maintaining productivity.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor power consumption, server performance, and demand patterns. This feedback loop enables real-time adjustment of power allocation and server configuration, allowing the system to maintain productivity during demand surges while staying within power budget limits and avoiding circuit breaker trips.
3Productivity
If servers are configured with different hardware for different cluster types, then service performance is improved, but power consumption variability increases making power management more difficult
Solution Approach 1:
The system applies local quality by tailoring power management strategies to specific server types and cluster requirements. Different hardware configurations within clusters receive customized power allocation and operational parameter adjustments based on their specific performance needs and power consumption characteristics, enabling optimized service performance while simplifying overall power management through localized control policies.
Data Source
AI summary
Embodiments are described for dynamically responding to demand for server computing resources. The embodiments can monitor performance of each of multiple computing systems in a data center, identify a particular computing system of the multiple computing systems for allocation of additional computing power, determine availability of an additional power supply to allocate to the identified computing system, and selectively enable or disable a turbo mode of processors associated with the computing devices.