Data Center Energy Dispatch Using User-Defined Consumption Levels
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Solution Overview
Problem
Cloud service providers face challenges in managing energy consumption in their networks and data centers, leading to increased costs and environmental impact, as existing technologies lack effective methods to tailor energy usage according to user-defined preferences and sustainable energy sources.
Innovation Solution
Implementing a data center and network architecture that partitions resources into high, medium, and low energy consumption partitions, allowing users to specify energy consumption levels and sources, with dynamic dispatching and routing mechanisms to optimize energy usage based on user-defined configurations, and utilizing an Infrastructure Processing Unit (IPU) to offload computationally intensive tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If cloud service providers increase energy consumption to improve service performance and speed, then service quality is improved, but energy cost and environmental impact worsen
Solution Approach 1:
The patent implements dynamic energy consumption configuration where the system can adjust energy usage parameters in real-time based on service requirements. The configurable energy consumption settings allow the system to dynamically optimize between service performance and energy efficiency, selecting appropriate energy consumption levels for different service scenarios rather than operating at fixed high-performance states.
Solution Approach 2:
The system changes operational parameters by allowing configuration of energy consumption settings at multiple levels (system-wide, application-level, and hardware-level). This enables the service provider to adjust energy parameters according to specific service requirements, transforming the static energy consumption model into a flexible, parameterizable system that can optimize performance-energy tradeoffs.
2Reliability
If cloud service providers use high-performance hardware components to ensure timely service delivery, then service reliability is improved, but energy consumption and operational costs worsen
Solution Approach 1:
The patent applies local quality by enabling different energy consumption configurations for different components and applications within the data center. Instead of uniformly high-performance hardware everywhere, the system allows specific hardware components and applications to be configured with appropriate energy consumption levels based on their specific reliability requirements and service criticality, optimizing the balance between reliability and energy efficiency at each location.
Solution Approach 2:
The system segments energy management into multiple levels: system-wide energy policies, application-specific energy configurations, and hardware-level power states. This segmentation allows different parts of the data center infrastructure to be managed independently with appropriate energy settings, enabling high-reliability services to receive optimized energy allocation without forcing all components to operate at maximum energy consumption.
3Adaptability or versatility
If cloud service providers deploy multiple hardware components to handle diverse service requirements, then service versatility is improved, but device complexity and management difficulty worsen
Solution Approach 1:
The patent implements universality through a unified energy consumption configuration framework that works across diverse hardware components and service types. The configurable energy parameters and policies provide a universal interface for managing energy across different applications, hardware platforms, and service scenarios, simplifying the management of complex heterogeneous infrastructure through consistent energy management principles.
Solution Approach 2:
The system introduces an intermediary layer (the energy consumption configuration system) between the diverse hardware components and the service requirements. This intermediary provides standardized energy management interfaces and policies that mediate between various hardware platforms and service needs, reducing the complexity of directly managing diverse hardware components by abstracting energy management through a unified configuration framework.
4Loss of energy
If cloud service providers manually optimize energy consumption for each application, then energy efficiency is improved, but operational time and resource expenditure worsen
Solution Approach 1:
The patent applies preliminary action by providing pre-configured energy consumption templates and default policies that can be applied to applications and hardware components before they are deployed or activated. This preliminary configuration reduces the time and resources needed for manual optimization, as the system comes pre-configured with energy management settings that can be adjusted as needed rather than requiring extensive manual tuning from scratch.
Solution Approach 2:
The system implements self-service through automated energy consumption optimization capabilities that can adjust energy settings without extensive manual intervention. The configurable energy policies and automated management features enable the system to self-optimize energy efficiency across applications and hardware, reducing the operational time and resource expenditure required for manual energy optimization while maintaining improved energy efficiency.
Data Source
AI summary
A method is described. The method includes receiving a request. The method includes allocating and/or configuring hardware to execute the request in accordance with an energy related input specified by a sender of the request. The method includes causing execution of the request in accordance with the energy related input.