Task Modeling System for Energy-Aware Multicore Scheduling
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Solution Overview
Problem
High-energy consumption in high-performance computing systems, particularly in 5G base stations, due to diverse workload configurations and varying computational resources, poses challenges for energy-efficient task distribution and resource scheduling.
Innovation Solution
A task modeling system that coordinates distributed learning across multiple computing platforms, using a global model to optimize latency and dependency-constrained multicore task scheduling while considering energy savings, allowing for novel platform generalization without additional training. This system dynamically adjusts clock speed and supply voltage to manage power consumption, allocating tasks to minimize energy usage within predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-performance multicore systems are used to handle diverse workload configurations in 5G base stations, then processing capability and productivity are improved, but energy consumption increases significantly
Solution Approach 1:
The system dynamically changes operational parameters (clock speed, supply voltage) of processing cores based on workload characteristics and energy constraints. The task modeling system adjusts these parameters in real-time to optimize the trade-off between processing capability and energy consumption, allowing high-performance systems to adapt their resource usage to match actual demands.
Solution Approach 2:
The patent implements dynamic task allocation and resource scheduling that adapts to varying workload configurations. The system continuously monitors and reassigns tasks across multiple cores based on current energy constraints and performance requirements, transforming a static resource allocation problem into a dynamic optimization process that responds to changing conditions.
2Use of energy by moving object
If tasks are distributed across multiple cores to improve energy efficiency, then energy consumption is reduced, but system complexity increases due to task scheduling and resource management
Solution Approach 1:
The task modeling system employs machine learning models that automatically learn optimal task allocation strategies from historical data and system behavior. This self-learning capability reduces the need for manual configuration and complex rule-based scheduling, allowing the system to autonomously manage task distribution across cores while optimizing for energy efficiency.
Solution Approach 2:
The system implements feedback mechanisms where the performance and energy consumption of task allocations are continuously monitored and used to refine future scheduling decisions. This closed-loop control approach allows the system to learn from past decisions and improve its task distribution strategy over time, reducing complexity through data-driven optimization rather than predetermined rules.
3Use of energy by moving object
If task allocation is optimized for energy savings, then power consumption is reduced, but latency constraints may be violated affecting processing speed
Solution Approach 1:
The system dynamically adjusts operational parameters (clock speed, supply voltage) of processing cores based on workload characteristics and energy constraints. The task modeling system adjusts these parameters in real-time to optimize the trade-off between processing capability and energy consumption, allowing high-performance systems to adapt their resource usage to match actual demands.
Solution Approach 2:
The patent implements dynamic task allocation and resource scheduling that adapts to varying workload configurations. The system continuously monitors and reassigns tasks across multiple cores based on current energy constraints and performance requirements, transforming a static resource allocation problem into a dynamic optimization process that responds to changing conditions.
Data Source
AI summary
A task modeling system, including a plurality of processing clients having a plurality of processing cores; a task modeler, including a memory storing an artificial neural network; and a processor, configured to receive input data representing a plurality of processing tasks to be completed by the processing client within a predefined time duration; and implement its artificial neural network to determine from the input data an assignment of the processing tasks among the processing cores for completion of the processing tasks within the predefined time duration, and determine a power management factor for each of the plurality of processing cores for power management during the predefined time duration; wherein the artificial neural network is configured to select the power management factor for each of the plurality of processing cores to achieve a power usage within a predefined threshold for the plurality of processing cores during the predefined time duration.