Multi-GPU AI Task Scheduling Using Difficulty-Aware Node Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing load distribution algorithms in multi-GPU-based cloud environments fail to consider task difficulty, leading to service delays and inefficiencies due to the disparity between high-performance and low-performance hardware.
Innovation Solution
A method and apparatus for scheduling AI tasks based on task difficulty and node-specific status information, using indicators such as GPU utilization, token processing volume, and service level objectives to optimize task allocation across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If round-robin method is used for load distribution, then tasks are evenly distributed to nodes, but service delays occur in nodes assigned tasks with high difficulty
Solution Approach 1:
The patent changes the scheduling parameter from simple round-robin indexing to a composite scoring system that evaluates multiple factors including task difficulty, node performance, and current load status. This allows the system to dynamically select optimal nodes based on real-time conditions rather than fixed sequential assignment.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring node performance metrics, task completion times, and difficulty levels. This feedback is used to update node scores and adjust future task assignments, creating a closed-loop control system that adapts to changing conditions.
2Device complexity
If tasks are assigned without considering task difficulty, then distribution is simple, but the gap between high-performance and low-performance hardware widens
Solution Approach 1:
The patent applies local quality by assigning different weights to various performance metrics based on the specific characteristics of each node and task type. High-performance nodes receive higher weights for complex tasks, while lower-performance nodes are better suited for simpler tasks, optimizing overall system productivity.
Solution Approach 2:
The scheduling system is made dynamic by continuously updating node scores based on real-time performance data, task difficulty assessments, and changing system conditions. This allows the system to adapt to hardware variations and optimize resource allocation dynamically rather than using static assignment rules.
3Productivity
If multiple indicator data are collected for task scheduling, then task allocation is optimized, but system complexity increases
Solution Approach 1:
The patent segments the scheduling decision-making process into distinct components: task difficulty evaluation, node performance assessment, score calculation, and selection. This modular approach allows each component to be optimized independently while maintaining overall system efficiency.
Solution Approach 2:
The system introduces an intermediary scoring mechanism that synthesizes multiple indicator data into a single composite score for each node. This intermediary layer simplifies the final selection process while still considering all relevant factors, balancing comprehensiveness with decision-making efficiency.
Data Source
AI summary
The disclosure relates to a method for scheduling tasks related to artificial intelligence (AI) services in a multi-GPU-based cloud environment, and the method includes: collecting information about a plurality of nodes in a cluster of a cloud environment; obtaining, from a user terminal, a task related to an AI service provided by the cluster; detecting a plurality of indicator data for scheduling the task; and selecting a node to assign the task among the plurality of nodes using the plurality of indicator data.