GPU-On-Demand Energy Scheduling With Distributed Power Resources
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Solution Overview
Problem
Current energy management systems in data centers with GPUs face challenges in energy efficiency, integration of renewable energy sources, and accessibility, often resulting in high operational costs and environmental impact, while also limiting access to high-performance computing resources for smaller entities and individual researchers due to high costs and complexity.
Innovation Solution
A GPU-on-demand system that integrates a computing device with a GPU, an advanced Energy Management System (EMS), distributed power resources, a Large Language Model (LLM) for energy metric analysis, an API gateway for secure access, and a software module to manage energy usage dynamically, optimizing energy efficiency and accessibility across a communications network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional energy management systems are used to regulate power usage, then energy consumption is controlled, but the systems lack flexibility and intelligence to adapt dynamically to varying workloads
Solution Approach 1:
The energy management system dynamically adjusts power allocation to distributed energy resources based on real-time workload demands. The system monitors GPU utilization and automatically scales energy distribution, transforming a static management approach into a dynamic one that adapts to changing computational requirements without requiring manual intervention or complex reconfiguration.
Solution Approach 2:
The system implements continuous feedback loops where energy consumption data from GPUs is collected, analyzed, and used to adjust power allocation in real-time. This feedback mechanism enables the energy management system to learn from historical patterns and optimize energy distribution automatically, enhancing adaptability while maintaining manageable system complexity through data-driven decision-making.
2Object-affected harmful factors
If renewable energy sources are integrated into the computing ecosystem, then environmental impact is reduced, but seamless integration with distributed power resources becomes challenging
Solution Approach 1:
The patent introduces an intermediary energy management system that acts as a mediator between renewable energy sources and computational workloads. This intermediary layer handles the complexity of integrating variable renewable energy inputs by buffering, smoothing, and intelligently allocating power to GPUs, thereby simplifying the integration process and reducing the burden on both energy sources and computing devices.
Solution Approach 2:
The system dynamically adjusts operational parameters such as power consumption levels, voltage, and frequency of GPUs based on the availability and characteristics of renewable energy inputs. By changing these parameters in real-time, the system adapts to the variable nature of renewable energy sources, enabling seamless integration while maintaining optimal performance and reducing environmental impact.
3Ease of operation
If GPUs are made accessible through cloud-based services, then accessibility is improved, but costs associated with the services become steep
Solution Approach 1:
The patent segments the energy management functionality into distributed components that operate locally at edge devices and cloud platforms. This segmentation enables smaller entities to access GPU computing power through localized energy resources without incurring high centralized cloud costs, while still benefiting from on-demand access. The segmented architecture allows for more granular control over energy consumption and associated costs.
Solution Approach 2:
The system enables self-service energy management where distributed energy resources and computing devices autonomously negotiate and allocate power based on local conditions and demand. This self-service mechanism eliminates the need for expensive intermediary cloud services to mediate every transaction, allowing direct peer-to-peer energy and computing resource sharing that reduces operational costs while maintaining accessibility.
4Ease of operation
If smaller entities access GPUs through current solutions, then computing resources become available, but significant upfront investment in hardware is required
Solution Approach 1:
The patent enables smaller entities to access GPU computing capabilities through virtualized copies or containers of GPU resources rather than requiring physical ownership of expensive hardware. By copying and distributing virtual GPU instances across distributed energy resources, the system provides affordable access to computing power without the need for significant upfront hardware investment, maintaining ease of operation while reducing barriers to entry.
Data Source
AI summary
A GPU-on-demand system includes a computing device equipped with a graphics processing unit (GPU) and memory, an Energy Management System (EMS) and a distributed power resource, a database for storing energy metrics including energy expenditure, a Large Language Model (LLM) for processing the energy metrics so as to generate an energy management plan that defines when each of the plurality of distributed power resources shall be used, an API gateway comprising an API coupled to a network connection, the API gateway configured for providing external systems secure, on-demand access to the GPU, and, a software module executing on the computing device, the software module configured for managing the GPU-on-demand system according to the energy management plan.