Flexible Workload Cells for Edge Computing Adaptability

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Solution Overview

Problem

Conventional workload management techniques in distributed edge computing environments lack adaptability and the ability to dynamically form and manage workload cells or clusters based on environmental conditions, leading to inefficient resource allocation and task distribution among computing resources.

Innovation Solution

The system introduces a method for forming flexible workload cells by grouping compute nodes and associating them with workload cells, where processing tasks are distributed among compute nodes, edge devices, and network devices, using a centralized algorithm for collision-free trajectory planning and autonomous agents like AMRs to navigate and perform tasks, and a FlexCell conductor to manage and adapt workload cells based on telemetry data and QoS information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If fixed deployment of computing resources is used, then resource allocation is simple and stable, but adaptability to environmental conditions deteriorates

Engineering Contradiction:

ImproveadaptabilityVSAvoidcomplexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic workload cells that can be formed, modified, and dissolved based on environmental conditions. Compute nodes are dynamically assigned to workload cells rather than being fixed, allowing the system to adapt to changing conditions while maintaining manageable complexity through automated orchestration.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system segments computing resources into discrete workload cells that can be independently managed and configured. Each workload cell represents a logical partition that can be adapted without affecting other cells, enabling flexible resource allocation while maintaining system organization.

Inventive Principle:

Principle #1Segmentation

2Productivity

If conventional workload management is used, then system operation is simple, but task distribution efficiency deteriorates

Engineering Contradiction:

Improvetask distribution efficiencyVSAvoidcomplexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system continuously monitors environmental conditions and workload status, using this feedback to dynamically adjust workload cell configurations and compute node assignments. This feedback mechanism enables optimized task distribution while automating the complexity of management decisions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system pre-configures workload cells and compute node assignments based on anticipated conditions and historical data, allowing for proactive optimization of task distribution. This preliminary action reduces the need for reactive reconfiguration and improves overall efficiency.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If fixed computing resource allocation is used, then resource management is straightforward, but workload redistribution capability deteriorates

Engineering Contradiction:

Improveworkload redistribution capabilityVSAvoidcomplexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20220413915A1Flexible cluster formation and workload scheduling

Publication Date: 2022.12.29 INTEL CORP

AI summary

Techniques are disclosed for the cell/cluster formation of compute nodes and workload and processing resource scheduling. Compute nodes within an environment may be grouped (clustered) together to perform one or more designated workload tasks. The clustered compute nodes may be associated with (or assigned to) a workload cell formed to perform one or more identified task(s).