Media Processing Workflow Adaptation for Runtime Resource Constraints

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Solution Overview

Problem

Existing cloud-based computing services for media production workflows cannot dynamically adjust resource usage to optimize task execution due to the discarding of execution contexts after each task completion, leading to varying performance despite changing resource availability.

Innovation Solution

A system and method that dynamically modify media processing functions by using APIs to recognize resource constraints, synthesize workflows, and optimize resource deployment based on a priori knowledge of cloud resources, allowing for dynamic modification of constraints during runtime to achieve optimal resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If cloud-based computing services use container-based execution for media production tasks, then resource flexibility and scalability are improved, but execution context persistence is lost after each task completion

Engineering Contradiction:

Improveresource flexibilityVSAvoidexecution context

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system discards temporary container instances after task completion but recovers and persists the execution context through a state management mechanism. The context includes task metadata, processing parameters, and resource allocation information that is stored and reused for subsequent task optimization, resolving the contradiction between container disposal and context retention.

Inventive Principle:

Principle #34Discarding and recovering

2Ease of manufacture

If media processing functions are deployed with fixed resource constraints, then deployment simplicity is improved, but dynamic optimization capability deteriorates

Engineering Contradiction:

Improvedeployment simplicityVSAvoidexecution optimization

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The system implements dynamic constraint parameters that can be adjusted during runtime based on actual resource availability and task requirements. The optimization engine continuously monitors execution performance and modifies processing parameters, resource allocation, and task scheduling dynamically, transforming static deployments into adaptive systems that improve productivity without complicating the initial deployment process.

Inventive Principle:

Principle #15Dynamics

3Productivity

If cloud resources are dynamically allocated based on task requirements, then resource utilization efficiency is improved, but system complexity increases

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system introduces an optimization engine as an intermediary layer between the media processing functions and the cloud infrastructure. This engine handles the complexity of dynamic resource allocation, constraint optimization, and task scheduling, while presenting a simplified interface to both the processing functions and the underlying cloud resources. The intermediary manages the complexity internally while maintaining ease of use externally.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If execution context is discarded after each task, then resource isolation and security are improved, but performance optimization across tasks deteriorates

Engineering Contradiction:

Improveresource isolationVSAvoidcross-task optimization

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system segments the execution context into isolated task-specific contexts that are independently managed for each media processing function. Each task receives a dedicated context with appropriate resource allocations and constraints, ensuring isolation and security. Simultaneously, the system maintains a higher-level optimization state that persists across tasks, enabling performance optimization without compromising the isolation benefits of individual task contexts.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12474963B2System and method for dynamically modifying media processing functions to control optimization of a media production

Publication Date: 2025.11.18 GRASS VALLEY CANADA

AI summary

A system is provided for dynamically modifying media processing functions to control optimization of a media production. The system includes a media processing function library that stores media processing functions that each include a subgraph embedded therein that has pre-calculated parameters and undefined variable parameters. Media processing engines execute the plurality of media processing functions to create the media production. Moreover, the system includes a media function management controller that places the media processing functions in a job queue to be executed by one or more of the media processing engines. A resource manager receives a media production instruction from a client device to partially deploy the media processing functions in the job queue. During execution, the media processing engine can receive an input deliverable that constrains the undefined variable parameters, such that the partially deployed media processing functions is dynamically modified during runtime to create the media production.