Load Balancing Machine Learning Modules via Resource Metrics
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Solution Overview
Problem
Selecting the appropriate machine learning algorithm for a specific task is challenging due to the numerous alternatives available, each with varying suitability, and existing solutions are tightly coupled to tasks, making effective execution and arrangement of machine learning algorithms difficult.
Innovation Solution
A computer-implemented method and system that includes discrete software modules with machine learning algorithms, a data store, and a message handler to determine domain parameters and generate output data, utilizing metrics for resource utilization and load-balancing algorithms to select the appropriate module for tasks, allowing for efficient execution and communication of machine learning results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning algorithms are deployed to address complex data challenges, then the accuracy and capability of data analysis is improved, but the complexity of selecting and managing appropriate algorithms increases
Solution Approach 1:
The patent introduces an intermediary system comprising a message handler and load balancer that mediates between task requests and machine learning algorithms. The message handler receives requests, determines appropriate domain parameters, and the load balancer selects suitable algorithms based on resource utilization metrics, thereby simplifying the complexity of direct algorithm selection while maintaining high analysis accuracy
Solution Approach 2:
The system creates a universal platform that can handle multiple different machine learning algorithms through a common interface. The message handler and load balancer provide multi-functional capabilities to manage various algorithms (classification, regression, clustering, etc.) uniformly, allowing accurate data analysis across different task types without increasing selection complexity
2Productivity
If machine learning algorithms are tightly coupled to specific tasks, then the performance for that specific task is optimized, but the adaptability and reusability of the algorithms decrease
Solution Approach 1:
The patent segments the machine learning system into independent, discrete software modules, each implementing a specific algorithm. These modular components can be independently selected and executed by the load balancer based on task requirements, maintaining optimized performance for specific tasks while enabling flexible reusability across different contexts through the standardized message handler interface
3Productivity
If discrete software modules with load-balancing are used to manage machine learning algorithms, then the resource utilization efficiency is improved, but the system architecture complexity increases
Solution Approach 1:
The patent merges the message handling and load balancing functions into an integrated system architecture. The message handler and load balancer work together as a unified control mechanism that manages multiple discrete algorithm modules, improving resource utilization efficiency while containing architecture complexity through functional integration rather than complete decentralization
Data Source
AI summary
A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, each module having associated a metric of resource utilization by the module, the method including receiving a request for a machine learning task; and selecting a module from the plurality of modules for the task based on the metric associated with the module.