Heterogeneous Video Processing Cloud Ground Unit Segmentation
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Solution Overview
Problem
Modern video processing facilities face insufficient processing power during peak utilization, leading to inefficiencies and security concerns when relying on public cloud computing resources due to questionable security, upload bandwidth issues, and non-deterministic availability.
Innovation Solution
A system comprising a ground unit that preprocesses video content and delivers it to cloud computing units for further processing, utilizing proprietary encryption, hardware-based encryption, and streaming to ensure secure and efficient processing, while eliminating mezzanine files from cloud storage and providing guaranteed single-occupancy instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If public cloud computing resources are used for video processing during peak demand, then processing capacity is improved, but security and reliability deteriorate
Solution Approach 1:
The system segments video processing into two distinct phases: pre-processing performed locally at the facility using private resources, and final processing performed in the cloud using public resources. This segmentation allows the system to maintain security-critical operations locally while leveraging cloud capacity for non-critical processing, thus resolving the contradiction between processing capacity and security/reliability.
Solution Approach 2:
The system introduces an intermediary local processing facility that acts as a buffer between the secure environment and the public cloud. This intermediary performs initial video processing and prepares data for cloud processing, ensuring that sensitive operations never leave the secure local environment while still enabling cloud resource utilization for additional processing capacity.
2Productivity
If more processing power is physically located at the facility, then processing capacity is improved, but device complexity and cost increase
Solution Approach 1:
The system implements dynamic resource allocation where the processing architecture adapts based on demand. During peak demand periods, the system leverages cloud resources; during normal periods, it operates with local resources only. This dynamic approach allows the facility to scale processing capacity without permanently increasing infrastructure complexity or cost.
Solution Approach 2:
The local processing facility is designed to perform multiple functions: it handles all processing during normal operations and also serves as a secure pre-processing stage when cloud resources are utilized. This multi-functionality maximizes the utility of local infrastructure without requiring dedicated separate systems, thereby avoiding increased complexity.
3Quantity of substance
If video content is compressed at the endpoint before transfer to cloud, then upload bandwidth requirements are reduced, but processing time increases
Solution Approach 1:
The system performs preliminary compression of video content at the endpoint before transfer to the cloud. By pre-compressing the video data, the system reduces the volume of data that needs to be uploaded to cloud resources, thereby minimizing the impact of compression time on overall processing throughput. This preliminary action ensures efficient utilization of upload bandwidth without significantly impacting total processing time.
Data Source
AI summary
A system including one or more cloud computing units and a ground unit. The one or more cloud computing units may be configured to process video content. The ground unit may be configured to pre-process the video content and deliver the video content to the one or more cloud computing units.