Adaptive Media Transcoding via Token-Based Resource Allocation
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Solution Overview
Problem
Traditional hardware solutions in mobile networks are inefficient in handling varying traffic patterns, often leading to underutilization or overload due to the need for large or small server setups, which are not optimized for sudden spikes in content delivery demands.
Innovation Solution
A system and method for adaptively transcoding media in a communications network that dynamically adjusts transcoding parameters based on available CPU resources and channel bandwidth, using a token-based approach to determine when to bypass or reduce transcoding efforts, ensuring effective utilization of processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the hardware required for the worst case is significantly large, then the system can handle peak traffic demands, but operators buy a large number of servers which are not used effectively most of the time, resulting in resource underutilization
Solution Approach 1:
The system dynamically adjusts the number of active transcoding servers based on real-time traffic conditions. During peak demand, additional servers are activated; during low demand, servers are deactivated or placed in standby mode. This dynamic scaling allows the system to maintain reliability for worst-case scenarios while avoiding resource underutilization during normal operation.
Solution Approach 2:
The system changes operational parameters such as CPU allocation, memory allocation, and server activation thresholds based on traffic patterns. By adjusting these parameters dynamically, the system can optimize resource utilization while maintaining the capability to handle peak traffic demands effectively.
2Loss of energy
If the number of servers are small, then resource utilization is improved, but the optimization solution can get overloaded (e.g., in terms of CPU and memory) when there is a sudden spike in optimizable heavy content
Solution Approach 1:
The system performs preliminary actions by pre-configuring server pools and establishing load balancing mechanisms before traffic spikes occur. When traffic increases, the pre-configured infrastructure can immediately handle the load without system overload, while maintaining efficient resource utilization during normal conditions.
Solution Approach 2:
The system implements continuous monitoring of CPU usage, memory consumption, and traffic patterns, using this feedback to dynamically adjust server activation and resource allocation. This feedback loop prevents system overload by detecting capacity thresholds and activating additional resources before overload occurs, while maintaining efficient utilization during normal operation.
3Device complexity
If traditional hardware solutions are used, then the system structure is simple, but the system is unable to effectively deal with rapidly changing traffic patterns
Solution Approach 1:
The system employs universal server platforms that can perform multiple functions - acting as standard servers during normal operation and transitioning to high-performance transcoding servers during peak demand. This multi-functionality allows the system to maintain simple architecture while gaining the adaptability to handle varying traffic patterns effectively.
Data Source
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AI summary
Systems and methods are described herein for adaptively transcoding media in a communications network such that computer processing resources are effectively utilized to deal with varying traffic patterns in the communications network. A computing device receives a request to transcode content and determines a first amount of tokens based on a number of CPU cores associated with the computing device, and a currently used amount of tokens. The computing device determines a second amount of tokens associated with transcoding the content and when the first amount of tokens is less than the second amount of tokens, the computing device bypasses transcoding for the content or adjusts a transcoding parameter to reduce a number of tokens associated with transcoding the content.