Edge Energy Demand Modeling for Task-Based Load Distribution
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Solution Overview
Problem
Current energy demand management studies for multi-access Edge Computing networks do not adequately consider energy management for network resources, particularly in a multi-access network environment, and fail to account for the diversity of tasks and energy sources.
Innovation Solution
A method and system for generating an energy demand model by classifying and categorizing computing tasks across various network infrastructure components, including access points, base stations, and edge servers, using a multi-access edge controller to predict energy demand and optimize the ratio of commonly used and alternative energy sources based on task groups and energy consumption patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If energy demand management studies focus only on smart grid and home appliances, then energy management for industries and appliances is improved, but energy management for multi-access network resources is neglected
Solution Approach 1:
The patent segments energy demand management into distinct components: smart grid energy management and multi-access network energy management. By creating separate but coordinated management systems for network resources (base stations, access points, edge servers), the patent ensures comprehensive coverage without compromising existing smart grid management capabilities.
Solution Approach 2:
The patent develops a universal energy demand model that can be applied across multiple domains including smart grid, home appliances, and multi-access network resources. This multi-functional approach allows the same analytical framework to serve different energy management needs, thereby improving versatility while maintaining specialized effectiveness.
2Measurement precision
If energy demand model considers diversity of tasks by generating model using task matched to one or more groups, then task-specific energy management is improved, but model complexity increases
Solution Approach 1:
The patent segments tasks into different groups (real-time tasks, non-real-time tasks, suspendable tasks) and creates specific energy demand models for each group. This segmentation allows precise energy prediction for each task type while maintaining manageable model complexity through modular structure.
Solution Approach 2:
The patent applies different energy demand model parameters and characteristics to different task groups based on their specific requirements. Each task group receives a customized energy management approach tailored to its properties, improving prediction accuracy without requiring a completely complex unified model.
3Productivity
If energy loads are distributed for each access edge server according to energy demand model, then energy management efficiency is improved, but computational overhead increases
Solution Approach 1:
The patent performs preliminary energy demand modeling and task grouping in advance, before actual energy load distribution is needed. By pre-classifying tasks and pre-establishing energy demand patterns, the system reduces real-time computational overhead while maintaining high energy management efficiency during operation.
Solution Approach 2:
The patent implements a dynamic energy load distribution mechanism that adapts to changing conditions. The energy demand model is updated based on actual task patterns and energy consumption, allowing the system to optimize efficiency while reducing computational overhead through learned patterns rather than constant recalculation.
Data Source
AI summary
The present disclosure relates to a method for generating an energy demand model by a multi-access edge server, the method including: a step a of receiving a task execution request signal including a bit value from a user; a step b of matching a task to one or more pre-classified groups using the bit value; a step c of extracting a feature of energy consumed to perform the task according to a criterion set differently for each group; a step of performing the steps a to c for one or more task execution request signals received from one or more users during a time period and generating an energy demand pattern for the time period using a feature of energy for one or more tasks performed during the time period; and a step of generating an energy demand model by time using one or more energy demand patterns by time and energy cost by time.