Cloud Production Management System for Media Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of obtaining and managing cloud and hybrid-cloud production resources from various vendors deters media production facilities from leveraging scalable, cost-effective cloud resources.
Innovation Solution
A cloud and hybrid-cloud production management system that uses a metering environment with software code accessing all required resources via APIs, combined with a machine learning model to optimize resource allocation, providing consolidated management and tracking of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If media production facilities use cloud-based resources from multiple vendors, then resource scalability and cost-effectiveness improve, but logistical complexity and management difficulty increase
Solution Approach 1:
The patent consolidates multiple cloud vendor resources and on-premises resources into a single unified management platform. The system aggregates computing resources, storage resources, bandwidth resources, and software licenses from different vendors and presents them as a unified pool, allowing media production facilities to manage all resources through one interface rather than dealing with each vendor separately.
Solution Approach 2:
The management platform performs multiple functions including resource provisioning, usage tracking, billing aggregation, and optimization across diverse cloud and on-premises resources. It serves as a universal control system that can handle different resource types from different vendors through standardized APIs, eliminating the need for separate management systems for each vendor.
2Adaptability or versatility
If media production facilities obtain resources from multiple vendors, then resource availability improves, but management overhead increases
Solution Approach 1:
The patent introduces a unified management platform as an intermediary layer between media production facilities and multiple cloud vendors. This platform translates facility resource requests into vendor-specific commands and aggregates vendor responses into unified status information. The intermediary handles all vendor interactions through standardized APIs, shielding facilities from vendor-specific complexities while maintaining access to diverse resources.
Solution Approach 2:
The system segments resource management into distinct functional modules including provisioning, monitoring, billing, and optimization. Each module handles specific aspects of multi-vendor resource management independently, allowing the platform to manage complex multi-vendor environments through organized, modular operations rather than monolithic management processes.
3Reliability
If traditional on-premises resources are used, then control over IT architecture is maintained, but scalability and cost-effectiveness are limited
Solution Approach 1:
The patent enables dynamic resource allocation that allows media production facilities to flexibly switch between on-premises and cloud resources based on demand. The unified management platform continuously monitors resource utilization and automatically provisions additional cloud resources when on-premises capacity is insufficient, then de-provisions cloud resources when demand decreases, optimizing the balance between control and scalability.
Solution Approach 2:
The system creates a hybrid infrastructure that combines on-premises and cloud resources into a unified resource pool. Rather than choosing one approach, the platform integrates both on-premises controlled resources and scalable cloud resources, allowing facilities to maintain architectural control where needed while leveraging cloud scalability where beneficial, creating a composite resource architecture.
Data Source
AI summary
A system includes a processor, and a memory storing software code and a machine learning (ML) model trained to allocate media production resources. The processor executes the software code to receive data describing a media flow requiring processing, identify, using the data and the ML model, media production resources for processing the described media flow, obtain the media production resources, and aggregate, from each of the media production resources, performance and billing metrics of a respective one of the media production resources resulting from processing of the described media flow by the media production resources. The processor may further execute the software code to determine, using the aggregated performance and billing metrics, a resource allocation efficiency score corresponding to each of one or more of the media production resources to provide one or more resource allocation efficiency score(s), and further train, using the resource allocation efficiency score(s), the ML model.