Dynamic Redundancy Control in Streaming Content Encoder Pools
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content streaming systems face challenges in maintaining optimal redundancy levels for content encoding, as high redundancy is resource-intensive while low redundancy may lead to downtime, and manual adjustments based on demand are impractical due to variability and volatility of demand.
Innovation Solution
A dynamic redundancy model using pools of redundant or cooperative content encoders managed by a pool manager that adjusts the number and configuration of encoders based on consumption data to ensure appropriate redundancy levels, minimizing downtime for high-demand content and reducing resource usage for low-demand content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high redundancy levels are maintained for content encoding, then system reliability and availability are improved, but resource consumption and operational costs increase
Solution Approach 1:
The patent implements dynamic redundancy adjustment where the pool manager continuously monitors consumption data and modifies the number of active encoder instances in real-time. High-demand content receives increased redundancy with multiple active encoder instances, while low-demand content is served by fewer instances, transforming the static redundancy model into a dynamic one that adapts to changing conditions.
Solution Approach 2:
The system changes the parameter of encoder instance count based on demand metrics. The pool manager adjusts redundancy levels by modifying the number of active encoder instances allocated to different content streams, thereby optimizing the balance between reliability and resource consumption through parameter variation.
2Loss of energy
If manual adjustments to redundancy levels are implemented, then resource optimization is improved, but system complexity and operational overhead increase
Solution Approach 1:
The pool manager autonomously monitors consumption data and adjusts encoder pool configurations without human intervention. The system self-regulates redundancy levels by automatically provisioning or de-provisioning encoder instances based on real-time demand metrics, eliminating the need for manual operational adjustments.
Solution Approach 2:
The system implements a feedback loop where consumption data from content delivery is continuously monitored and fed back to the pool manager, which then adjusts redundancy levels accordingly. This closed-loop control enables automatic optimization of resource allocation based on actual system performance and demand patterns.
3Productivity
If redundancy levels are dynamically adjusted based on demand, then resource efficiency is improved, but system complexity and computational overhead increase
Solution Approach 1:
The pool manager serves multiple functions: it monitors consumption data, determines demand levels, decides on redundancy adjustments, and provisions encoder instances. This multi-functional component consolidates various system tasks into a single management entity, reducing overall system complexity while enabling dynamic resource optimization.
Solution Approach 2:
The pool manager acts as an intermediary layer between the content delivery system and the encoder pool infrastructure. It abstracts the complexity of dynamic redundancy management by handling all adjustment logic centrally, allowing the underlying encoder instances to operate with simplified configurations while still achieving optimized resource efficiency.
Data Source
AI summary
Systems and methods are described to enable synchronized encoding of streaming audio or video content between multiple encoders, in a manner that provides for redundancy of the system to vary based on a demand for the output content. End user devices or content distribution systems can monitor how content is output on end user devices, and report such output to a content encoding system. The encoding system can then redundancy provided for streaming content based on the demand by end users. Streams that are in high demand can be processed with high redundancy among devices that provide seamlessly interchangeable content, thus reducing the likelihood of perceived failure for such streams. Streams that are in low demand can be processed with low redundancy, reducing the computing resources used to process the stream while minimizing the overall impact of a processing failure, should one occur.