Distributed Compute Node Allocation for Data Center Power Balancing
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Solution Overview
Problem
The increasing power consumption of AI-driven data centers poses challenges related to sustainability, cost, and resource allocation, necessitating the development of more efficient power management systems and decentralized computing solutions.
Innovation Solution
A distributed compute system with geographically dispersed compute nodes, including residential units and commercial data centers, managed by a power management unit that monitors and optimizes power consumption to allocate tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If AI computation is performed in centralized data centers, then computational power and processing capability are improved, but power consumption and energy costs increase significantly
Solution Approach 1:
The patent segments the centralized data center into distributed compute nodes deployed across multiple locations including residential units, commercial buildings, and edge sites. Each node independently performs AI inference tasks, distributing the computational workload geographically to reduce energy consumption at any single location and utilize available capacity more efficiently across the network.
Solution Approach 2:
The patent transitions from a single-dimensional centralized data center model to a multi-dimensional distributed architecture that spans residential, commercial, and edge environments. This dimensional expansion allows the system to leverage unused computational capacity across diverse locations, reducing overall power consumption while maintaining computational power through spatial distribution.
2Loss of energy
If more compute nodes are deployed in distributed locations, then energy efficiency is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent introduces a coordination layer that acts as an intermediary between distributed compute nodes and task requesters. This intermediary manages task allocation, monitors power consumption levels, and coordinates inference requests across nodes, thereby reducing the complexity burden on individual nodes while maintaining energy efficiency through centralized optimization.
Solution Approach 2:
The patent implements feedback mechanisms where compute nodes report their power consumption status and availability to the coordination system. This feedback enables dynamic task allocation that adapts to real-time energy conditions, optimizing energy efficiency while the coordination system manages the complexity of coordinating multiple distributed nodes through standardized communication protocols.
3Use of energy by moving object
If compute nodes are placed in residential units, then power consumption at data centers is reduced, but reliability and stability of service may be compromised
Solution Approach 1:
The patent changes the operational parameters of distributed compute nodes by configuring them to operate in different modes based on their location and stability characteristics. Residential nodes may operate during off-peak hours or for less critical inference tasks, while more stable commercial and edge nodes handle time-sensitive workloads. This parameter-based differentiation maintains service reliability while reducing overall data center power consumption.
Solution Approach 2:
The patent applies local quality by assigning different roles and reliability requirements to compute nodes based on their deployment location. Residential units may provide capacity for non-critical tasks, commercial buildings handle medium-priority workloads, and edge data centers manage critical time-sensitive operations. This localized quality assignment ensures service reliability is maintained for important tasks while utilizing residential capacity for less demanding work to reduce data center power consumption.
Data Source
AI summary
A distributed compute system may include a plurality of compute nodes located in geographically distributed sites. A data processing task is performed in a distributed manner among the plurality of compute nodes. The distributed compute system may include a first power management unit located in a first site of the geographically distributed sites. The first site consumes electrical power for both a first compute nodes of the plurality of compute nodes and for a non-data processing load. The first power management unit monitors power consumption at the first site. An allocation of the data processing task to the first compute node is controlled at least partially based on power consumption at the first site monitored by the first power management unit.