AI Inferencing Workload Placement for Latency-Aware Heterogeneous Environments

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Solution Overview

Problem

Existing computing systems face challenges in optimizing the placement of artificial intelligence workloads across heterogeneous production environments, leading to inefficiencies in latency, completion time, and security considerations.

Innovation Solution

A workload placement service that determines optimal production environments for AI workloads based on latency minimization, completion time minimization, and security considerations, using telemetry data and causal variables to adjust placements dynamically.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If AI workloads are assigned to computing devices in a heterogeneous environment, then productivity is improved through distributed computing capabilities, but latency increases due to varying device performance and network conditions

Engineering Contradiction:

ImproveAI workload execution efficiencyVSAvoidInferencing latency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system dynamically adjusts workload placement decisions based on real-time telemetry data, device performance metrics, and network conditions. The workload placement service continuously monitors and re-evaluates optimal device assignments, transforming a static assignment problem into a dynamic optimization process that adapts to changing system states to minimize latency while maintaining productivity

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements a feedback loop where telemetry data from computing devices and network performance metrics are continuously collected and fed back to the workload placement service. This feedback mechanism enables the system to learn from past placement decisions and their outcomes, adjusting future assignments to improve latency performance while maintaining efficient resource utilization

Inventive Principle:

Principle #23Feedback

2Ease of operation

If workload placement is optimized for latency minimization, then user experience is improved, but device complexity increases due to the need for dynamic placement decisions and telemetry processing

Engineering Contradiction:

ImproveUser experience consistencyVSAvoidWorkload placement management complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The workload placement service acts as an intermediary layer between AI workloads and computing devices, centralizing the complex logic for latency optimization and telemetry processing. This mediator absorbs the complexity of dynamic placement decisions, performance monitoring, and device coordination, while presenting a simplified interface to both workloads and end users, thereby improving user experience without proportionally increasing overall system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If heterogeneous computing devices are utilized for AI workloads, then adaptability is improved through diverse hardware capabilities, but performance consistency deteriorates across different devices

Engineering Contradiction:

ImproveHardware configuration flexibilityVSAvoidPerformance consistency

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system applies local quality optimization by tailoring workload placement decisions to the specific characteristics of each computing device and its network conditions. Rather than applying uniform placement rules, the workload placement service customizes assignments based on individual device performance profiles, hardware capabilities, and real-time telemetry data, enabling heterogeneous devices to contribute effectively while maintaining overall performance consistency across the distributed system

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250238274A1Artificial intelligence inferencing workload placement to minimize latency in a heterogeneous environment

Publication Date: 2025.07.24 DELL PROD LP

AI summary

A method for managing inferencing workload placement based on latency minimization includes performing an initial workload placement of the inferencing workload to assign the inferencing workload to a first production environment of the plurality of production environments, after performing the initial workload placement, monitoring: execution of the inferencing workload on the first production environment, and communication between the first production environment and a front-end environment, to obtain telemetry data associated with the execution and the communication, performing a latency analysis using the telemetry data to generate a placement recommendation, making a determination that the placement recommendation specifies a second production environment of the plurality of production environments, and based on the determination, initiating deployment of the inferencing workload to the second production environment.