ML Model Scheduling With Automated Cluster Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ML model processing approaches face challenges in managing schedule and resource allocation in heterogeneous computing environments, particularly in edge computing, due to issues like cold-start times, workload scheduling, and manual configuration of millions of execution locations, which are inefficient and impractical for developers.
Innovation Solution
A cluster-less, server-less, and model-less INFaaS platform that automatically manages ML model deployment and execution, selecting appropriate environments based on service level agreements and optimizing for accuracy, cost, and performance across edge, cloud, and core computing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of execution locations is used in heterogeneous computing environments, then deployment flexibility is improved, but operational complexity and time consumption increase significantly
Solution Approach 1:
The system automatically discovers available execution locations and configures model deployment without manual intervention. The automated deployment mechanism queries the environment for suitable execution locations, evaluates them against criteria, and selects optimal locations autonomously, eliminating the time-consuming manual configuration process while maintaining deployment flexibility.
Solution Approach 2:
The deployment system dynamically adapts to heterogeneous computing environments by automatically detecting and evaluating available execution locations at runtime. Rather than static pre-configuration, the system continuously assesses environment capabilities and adjusts deployment decisions based on current conditions, enabling flexibility without manual overhead.
2Productivity
If automated cluster selection based on metrics is implemented, then scheduling efficiency is improved, but system complexity increases
Solution Approach 1:
The complex scheduling task is segmented into distinct components: metric collection from execution locations, metric evaluation against criteria, and cluster selection based on evaluated metrics. This modular approach improves scheduling efficiency by processing each aspect separately while managing system complexity through clear separation of concerns in the automated deployment mechanism.
Solution Approach 2:
An automated deployment mechanism acts as an intermediary between the ML model deployment request and the heterogeneous computing environment. This intermediary handles the complex task of evaluating multiple metrics and selecting appropriate clusters, shielding users from complexity while maintaining high scheduling efficiency through automated decision-making.
3Power
If multiple clusters are used for ML model execution, then processing capacity is improved, but resource management complexity increases
Solution Approach 1:
The automated deployment mechanism provides a universal interface for managing ML model execution across multiple heterogeneous clusters. It evaluates execution locations against common criteria and applies consistent selection logic regardless of the specific cluster characteristics, enabling increased processing capacity while simplifying resource management through a unified approach.
Solution Approach 2:
The system manages multiple clusters by evaluating and comparing their parameters (metrics) against deployment criteria. It dynamically selects clusters based on parameter matching rather than fixed assignments, allowing flexible utilization of multiple clusters for increased processing capacity while automating the complex parameter-based resource management.
Data Source
AI summary
Techniques are disclosed for schedule management for machine learning model-based processing in a computing environment. For example, a method receives a machine learning model-based request and determines a scheduling decision for execution of the machine learning model-based request. Determination of the scheduling decision comprises identifying, based on one or more metrics, at least one cluster from a plurality of clusters as an execution environment in which the machine learning model-based request is to be executed. The machine learning model-based request may then be forwarded to the at least one identified cluster for execution.