Carbon Footprint-Aware Computing Resource Allocation
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Solution Overview
Problem
High-performance computing (HPC) environments face challenges in reducing their ecological impact due to inefficient energy consumption, as existing work managers only consider qualitative energy efficiency criteria, not accurately accounting for real energy usage.
Innovation Solution
A method to dynamically calculate a global carbon footprint for each job, integrating it as a resource allocation criterion in the work manager, considering both the usage and material carbon footprints of computing resources, allowing for more precise ecological impact assessment and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If qualitative energy efficiency criteria are used for resource allocation, then the allocation process is simple, but the accuracy of energy consumption assessment is insufficient
Solution Approach 1:
The patent transitions from qualitative energy efficiency criteria to quantitative carbon footprint parameters. It introduces dynamic parameters including electricity consumption, heating/cooling demands, and renewable energy generation specific to each time slot, enabling precise measurement of energy consumption impact while maintaining allocation process manageability through structured parameter organization.
Solution Approach 2:
The patent replaces the traditional job manager's simple scheduling mechanism with an enhanced system that integrates carbon footprint calculation and optimization. This substitution introduces new computational layers that calculate time-slot-specific carbon footprints and modify allocation decisions based on these quantitative assessments, thereby improving measurement precision without excessive complexity increase.
2Object-affected harmful factors
If traditional resource allocation methods are used, then the allocation process is fast, but the ecological impact is not optimized
Solution Approach 1:
The patent performs preliminary calculations of carbon footprint parameters for each available time slot before making allocation decisions. By pre-computing electricity consumption, thermal demands, and renewable generation metrics for all candidate time slots, the system enables fast comparison and selection of optimal allocations without sacrificing ecological optimization during the actual allocation process.
Solution Approach 2:
The patent introduces carbon footprint calculation as an intermediary step between resource availability assessment and final allocation decision. This intermediary layer translates physical energy consumption data into comparable ecological impact metrics, allowing the system to optimize for reduced harmful environmental factors while maintaining allocation efficiency through structured intermediate representations.
3Reliability
If static energy efficiency criteria are used, then the allocation is straightforward, but it does not reflect actual dynamic energy consumption
Solution Approach 1:
The patent transforms static energy efficiency criteria into dynamic time-slot-specific carbon footprint parameters. It captures temporal variations in electricity consumption patterns, heating and cooling demands, and renewable energy generation across different time slots. This dynamic approach accurately reflects actual energy consumption conditions while organizing complexity through structured time-slot-based parameter groups.
Data Source
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AI summary
One aspect of the invention relates to a method (100) for allocating computing resources to at least one job to be performed, comprising the steps: - receiving (101) at least one request for the allocation of computing resources to perform a job, the request including at least one hardware resource and at least one job execution time, - determining (102) the allocation from the request and the availability of the plurality of computing resources, each allocation including: o a set of computing resources from the plurality of computing resources (20), o an allocation date, o a usage time determined from the allocation date and the job execution time, for each allocation determined: - estimating (103) a carbon footprint from a hardware carbon footprint and a usage carbon footprint,- creation (104) of a list including each determined allocation and its associated estimated carbon footprint, - reception (105), by the IT infrastructure (2), of an allocation from among the allocations in the list, - allocation (106).