Modular Audiovisual Processing System with Dynamic Scaling
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Solution Overview
Problem
Current audiovisual content distribution systems are monolithic and static, making them inflexible and difficult to scale, integrate new functionalities, or manage load balancing and scaling effectively, especially in dynamic environments like OTT services where unmanaged networks with varying bandwidth and quality of service are common.
Innovation Solution
A modular system with elementary modules that include variable processing units, a central module for request handling, a scaling module for dynamic allocation of processing units based on a deep learning model, and a load balancing module for efficient resource distribution, allowing for dynamic reconfiguration of processing chains and agile handling of requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic static system is used for audiovisual content distribution, then system simplicity is maintained, but flexibility and scalability are poor
Solution Approach 1:
The system is divided into independent micro-services (transcoding service, packaging service, CDN management service, monitoring service) that can be deployed, scaled, and managed separately. Each micro-service corresponds to a specific functional module, enabling flexible configuration and scalability without affecting the entire system.
Solution Approach 2:
The system implements dynamic configuration capabilities where processing chains can be modified in real-time based on demand. The architecture allows dynamic addition/removal of micro-services, dynamic scaling of resources, and dynamic adjustment of processing parameters without system restarts.
2Adaptability or versatility
If processing chains are fixed and predefined, then system stability is maintained, but ability to integrate new functionalities is difficult
Solution Approach 1:
Processing chains are segmented into discrete, independently deployable micro-services. Each micro-service can be updated or replaced without affecting other parts of the system, enabling safe integration of new functionalities while maintaining overall system stability through isolated changes.
Solution Approach 2:
The system uses configuration files and manifests to define processing chains in advance, allowing new functionalities to be integrated through pre-planned configurations rather than complex runtime modifications. This enables stable, predictable system evolution.
3Productivity
If manual load balancing and scaling are used, then system complexity is reduced, but efficiency in handling dynamic network conditions is poor
Solution Approach 1:
The system implements self-service monitoring and alerting mechanisms that automatically detect performance degradation and trigger scaling actions. The monitoring service collects metrics, identifies bottlenecks, and initiates resource allocation without manual intervention, enabling efficient adaptation to dynamic conditions.
Solution Approach 2:
The system incorporates continuous feedback loops where performance metrics are collected, analyzed, and used to automatically adjust resource allocation and processing parameters. This closed-loop control enables efficient, adaptive load balancing and scaling based on real-time system conditions.
4Adaptability or versatility
If multiple protocols and profiles are supported with multiple versions of content, then compatibility with different terminals is improved, but data storage volume increases significantly
Solution Approach 1:
The system extracts and separates the protocol-specific packaging layer from the core content. A single standardized content format is used, with protocol-specific adaptations handled by separate packaging micro-services that generate only the necessary protocol-specific data structures, reducing redundant storage.
Solution Approach 2:
Instead of storing multiple versions of content with different parameters, the system stores content in a standardized format and dynamically adjusts parameters (encoding, resolution, bitrate) through processing micro-services based on terminal capabilities and network conditions, reducing storage requirements while maintaining compatibility.
Data Source
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AI summary
Elementary module of a processing chain of an audiovisual content distribution system, each content being received by a terminal in the form of a succession of segments, each segment being distributed to said terminal following a transmission of a request by said terminal and being obtained by an application of a processing chain to a portion of a content.The elementary module executes a processing of a predefined type and includes: a variable plurality of processing units available to execute said processing; a scaling module, capable of determining, using a first model, a number of processing units to allocate for an implementation of a set of the processing requested by said elementary module; and, a load management module capable of choosing, using a second model, for each processing requested, at least one processing unit from among the processing units allocated by the scaling module to perform said processing, each model being a deep learning neural network.