Workload Orchestrator for Contextual ML QoS Switching
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Solution Overview
Problem
The execution of machine learning model algorithms for AI productivity tools consumes significant system resources and impacts performance, despite variations in accuracy, speed, and processing requirements among different model algorithms.
Innovation Solution
Implementing a system and method for contextual quality of service (QoS) machine learning model algorithm selection, which includes a hardware processor executing computer-readable program code instructions to identify the most suitable ML model algorithm based on runtime telemetry data and QoS metrics, optimizing resource utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model algorithms are executed for AI productivity tools, then accuracy and functionality are improved, but system resource consumption increases and performance deteriorates
Solution Approach 1:
The system dynamically selects different ML model algorithms based on runtime QoS metrics and contextual conditions. The workload orchestrator continuously monitors system state and adjusts model selection in real-time, transitioning between different algorithm configurations to optimize the balance between accuracy and performance based on current operational requirements.
Solution Approach 2:
The system changes parameters of the ML model algorithms including model size, complexity, and computational requirements. By selecting from multiple algorithm variants with different parameter configurations, the system can adjust the trade-off between accuracy and resource consumption to match current QoS conditions.
2Measurement precision
If larger and more complex ML model algorithms are used, then accuracy is improved, but processing speed and resource efficiency deteriorate
Solution Approach 1:
The system dynamically adjusts model complexity based on runtime conditions. The workload orchestrator selects from multiple algorithm variants with different processing speeds and accuracy levels, adapting the choice to current QoS metrics such as latency requirements and available computational resources.
Solution Approach 2:
Different parts of the system use different ML model algorithms optimized for their specific requirements. The system applies local quality by selecting appropriate model complexities for different workloads and operational contexts, rather than using a single uniform model across all scenarios.
3Adaptability or versatility
If multiple ML model algorithms are available for selection, then adaptability to different conditions is improved, but system complexity increases
Solution Approach 1:
The workload orchestrator serves as an intermediary that manages the complexity of selecting from multiple ML model algorithms. It abstracts the decision-making process by monitoring QoS metrics and automatically selecting appropriate models, shielding higher-level system components from the complexity of algorithm selection while enabling adaptability.
Solution Approach 2:
The workload orchestrator performs multiple functions including monitoring QoS metrics, selecting appropriate ML models, and managing algorithm execution. This multi-functional approach consolidates complexity management in a single component that handles various aspects of adaptive model selection.
Data Source
AI summary
An information handling system includes a hardware processor executing computer-readable program code instructions of artificial intelligence (AI) productivity tool software module to identify a capability intent action associated with one or more AI productivity tool-enablable software applications via invocation of a first size-variant machine learning (ML) model algorithm to identify the capability intent action based on user-query input, and executing code instructions of a workload orchestrator to monitor execution of the first size-variant ML model algorithm for an identified operation to determine when to switch to execution to a second size-variant ML model algorithm or switch to a different hardware processor to execute the identified operation to maintain a quality of service (QoS) metric threshold for operation of the information handling system as well as precision of output for the size-variant ML model algorithm used.