Video File Processing via Segmented Software Containers
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Solution Overview
Problem
Current video processing technologies face inefficiencies due to underutilization of computing resources when processing video files across different hardware environments, leading to idle computing nodes and long processing times, as existing schedulers struggle to manage varying workloads and processing capacities.
Innovation Solution
The use of software containers configured with matching processing capacities for video segments allows for parallel processing and efficient scheduling, ensuring that each container is fully utilized, thereby completing video processing tasks in a deterministic time frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional schedulers are used to manage video processing workloads across different hardware environments, then applications can be deployed across multiple servers, but computing resources are underutilized leading to idle computing nodes and long processing times
Solution Approach 1:
The patent segments video processing workloads into discrete tasks that can be independently scheduled and distributed across multiple computing nodes. By dividing the monolithic video processing workflow into smaller, manageable segments, the system can efficiently allocate resources and process multiple video files in parallel, thereby improving throughput and reducing overall processing time
Solution Approach 2:
The patent implements dynamic workload scheduling that adapts to varying hardware capacities and processing speeds across different computing nodes. The scheduler continuously monitors resource utilization and dynamically reassigns video processing tasks to optimize load balancing, ensuring that computing resources are fully utilized and preventing idle nodes while minimizing processing time
2Productivity
If video processing tasks are distributed across multiple computing nodes, then processing capacity is increased, but resource utilization becomes unbalanced leading to idle nodes
Solution Approach 1:
The patent incorporates feedback mechanisms where the scheduler continuously monitors the status, load, and performance metrics of each computing node. Based on this real-time feedback, the system dynamically adjusts task allocation to maintain balanced resource utilization across all nodes, preventing both overloading and idle states while maximizing overall processing capacity
Data Source
AI summary
A technology is described for processing video files using a software container. An example method may include dividing a video file into video segments and distributing the video segments to software containers which provide an isolated environment for a video processing application by creating a virtual container in which the processing application is contained. The video segments are then processed using the video processing application contained in the software containers, and the video file may be reconstructed using processed video segments output by the video processing application.