Workflow Controller Segmentation for ML Resource Allocation
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Solution Overview
Problem
Current methods for deciding whether to serve machine learning models from on-premises or cloud environments are inefficient and lead to improper model serving due to manual decision-making by administrators, and existing cluster management for machine learning workflows results in resource wastage as different tasks have varying resource requirements.
Innovation Solution
A workflow controller segments machine learning workflows into sub-workflows based on resource needs and assigns appropriate clusters for each task, using a serving model to determine whether to serve models from local or remote environments based on parameters like latency, resource requirements, and security, thereby optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual decision-making by administrators is used to determine model serving environment, then human control over deployment decisions is maintained, but efficiency is reduced and improper model serving occurs due to inability to account for various factors
Solution Approach 1:
The system enables self-service through an automated model serving decision mechanism that evaluates multiple factors (latency requirements, resource availability, security constraints) and independently determines the optimal serving environment without requiring administrator intervention. This automated decision-making process improves both efficiency and correctness by consistently applying evaluation criteria.
Solution Approach 2:
The patent replaces the mechanical system of manual administrator decision-making with an automated computational system that uses structured evaluation of serving parameters. This substitution eliminates human limitations in processing multiple factors simultaneously while maintaining reliable decision-making through systematic assessment of latency, resources, and security requirements.
2Device complexity
If entire machine learning workflow is assigned to a single heterogeneous cluster, then workflow orchestration is simplified, but resource wastage occurs as high-resource training tasks dedicate clusters that are then underutilized by low-resource deployment tasks
Solution Approach 1:
The patent segments the machine learning workflow into distinct phases (training, evaluation, deployment) and assigns each phase to appropriate clusters based on resource requirements. This segmentation allows training tasks to use high-resource clusters while deployment tasks utilize lower-resource clusters, eliminating resource wastage without significantly increasing orchestration complexity through automated phase identification and cluster matching.
Solution Approach 2:
The system applies local quality by matching specific workflow phases to clusters with appropriate resource characteristics. Training phases are assigned to clusters with high computational resources, while deployment phases are assigned to clusters with moderate resources, ensuring each task runs in an environment optimized for its specific resource needs rather than using a uniform cluster assignment.
Data Source
AI summary
A method can include receiving, at a workflow controller, a machine learning workflow, the machine learning workflow associated with a first task and a second task. The first task is training a machine learning model and the second task is deploying the model. The method can include segmenting, by the workflow controller, the machine learning workflow into a first sub-workflow associated with the first task and a second sub-workflow associated with the second task, assigning a first workflow agent to the first sub-workflow and assigning a second workflow agent to the second sub-workflow, selecting, by the first workflow agent and based on first resources needed to perform the first task, a first cluster for performing the first task and selecting, by the second workflow agent and based on second resources needed to perform the second task, a second cluster for performing the second task.