Context-Sensitive Defragmentation of Containerized Workloads on Edge Devices

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Solution Overview

Problem

Manual deployment of containerized workloads on SD-WAN edge devices is laborious, error-prone, and results in less effective workload distribution, leading to resource underutilization and increased operating expenses due to the complexity of interdependent steps involved in securely deploying and managing workloads across edge infrastructure.

Innovation Solution

A computer-implemented method and system for context-sensitive defragmentation and aggregation of containerized workloads that monitors telemetry data from SD-WAN edge devices to identify inter-workload dependencies, initiating migration processes to optimize resource utilization and workload placement, ensuring secure and efficient deployment and management of workloads across edge devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual deployment methods are used for containerized workloads on edge devices, then deployment control and security can be maintained, but the process becomes laborious, error-prone, and results in less effective workload distribution

Engineering Contradiction:

Improvedeployment efficiencyVSAvoidoperational complexity

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system enables automated self-service deployment where the orchestration platform automatically discovers inter-workload dependencies, determines optimal placement, and executes migration without manual intervention. The platform monitors telemetry data, identifies dependency relationships between workloads, and autonomously performs defragmentation and aggregation operations, eliminating the need for manual deployment while maintaining security and control.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical deployment processes with an automated software-based orchestration system. The system uses telemetry data analysis and algorithmic dependency detection to substitute human-operated deployment steps with automated computational processes, thereby improving efficiency and reducing errors while maintaining deployment control through the orchestration platform.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If workloads are distributed across multiple edge devices, then system reliability and fault tolerance are improved, but resource underutilization occurs due to workload fragmentation

Engineering Contradiction:

Improvesystem reliabilityVSAvoidresource underutilization

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system segments workloads into modular containerized units that can be independently managed and migrated. By containerizing workloads, the system maintains flexibility in distribution across multiple edge devices for reliability while enabling dynamic reallocation to consolidate resources and eliminate underutilization. The orchestration platform can split workloads across devices for fault tolerance or aggregate them for resource efficiency based on real-time conditions.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements dynamic workload placement where the orchestration platform continuously monitors system state and adjusts workload distribution in real-time. Workloads are dynamically migrated between edge devices based on resource availability, dependency relationships, and performance requirements, allowing the system to adapt between distributed deployment for reliability and consolidated deployment for resource efficiency.

Inventive Principle:

Principle #15Dynamics

3Productivity

If inter-workload dependencies are not considered in workload placement, then deployment simplicity is maintained, but workload performance and coordination efficiency deteriorate

Engineering Contradiction:

Improveworkload coordination efficiencyVSAvoiddeployment complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The orchestration platform implements feedback mechanisms by continuously monitoring telemetry data from edge devices and analyzing inter-workload dependency relationships. The system uses this feedback to make informed placement decisions, automatically adjusting workload distribution to maintain performance and coordination efficiency while managing deployment complexity through automated dependency detection and resolution.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11729062B1Context-sensitive defragmentation and aggregation of containerized workloads running on edge devices

Publication Date: 2023.08.15 VELOCLOUD NETWORKS LLC

AI summary

Computer-implemented methods, media, and systems for context-sensitive defragmentation and aggregation of containerized workloads running on edge devices are disclosed. One example method includes monitoring telemetry data from multiple software defined wide area network (SD-WAN) edge devices that run multiple workloads, where the telemetry data includes at least one of resource utilization at the multiple SD-WAN edge devices, inter-workload trigger dependency, or inter-workload data dependency among the multiple workloads. It is determined, based on the telemetry data, that at least two of the multiple workloads running on at least two SD-WAN edge devices have the inter-workload trigger dependency or the inter-workload data dependency. In response to the determination that the at least two of the multiple workloads have the inter-workload trigger dependency or the inter-workload data dependency, a first process of migrating the at least two of the multiple workloads to a first SD-WAN edge device of is initiated.