User-Defined Energy Control for Data Center Resource Partitioning
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Solution Overview
Problem
Cloud service providers face challenges in managing energy consumption in their networks and data centers, as existing technologies lack efficient methods to align energy usage with user-defined preferences and environmental sustainability goals.
Innovation Solution
The implementation of a data center and network architecture that allows end users to define their energy consumption configurations, which are then used to partition resources into high, medium, and low energy consumption partitions, ensuring that energy usage aligns with user-defined budgets and source preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data centers use high energy consumption resources to ensure service quality and speed, then service delivery efficiency is improved, but energy costs and environmental impact increase
Solution Approach 1:
The system dynamically adjusts resource allocation based on user-defined energy budgets. The energy consumption configuration is not fixed but adaptable, allowing the data center to optimize between service quality and energy usage in real-time based on user preferences and environmental conditions
Solution Approach 2:
The invention changes the operational parameters of data center resources by introducing user-defined energy consumption configurations. This allows the system to operate resources at different energy levels (high, medium, low) depending on user preferences, thereby reducing energy consumption while maintaining acceptable service delivery
2Loss of energy
If data centers partition resources into different energy consumption levels, then energy cost reduction is achieved, but system complexity increases
Solution Approach 1:
The data center resources are segmented into different energy consumption partitions (high, medium, low). This segmentation allows the system to allocate resources based on user-defined energy budgets, reducing overall energy consumption by directing workloads to appropriate energy levels without requiring complete system redesign
Solution Approach 2:
The energy consumption configuration system serves multiple functions: it manages energy costs, provides user control over environmental impact, maintains service quality, and simplifies decision-making through automated resource allocation. This multi-functionality offsets the added complexity by delivering comprehensive value
3Object-affected harmful factors
If data centers allow user-defined energy consumption configurations, then environmental sustainability is improved, but control and management complexity increases
Solution Approach 1:
The system enables users to self-configure their energy consumption preferences without requiring complex manual management. Users can define their own energy budgets and the system automatically translates these into resource allocation decisions, reducing the need for complex human intervention while improving environmental sustainability
Data Source
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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.