Dynamic Channel Change Server Allocation via Neural Network Prediction
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Solution Overview
Problem
Existing media content distribution systems face inefficiencies in channel change server allocation, leading to resource wastage or service degradation due to either over- or under-allocation of active channel change servers, which affects the speed of channel changes and user experience.
Innovation Solution
A server-based system that dynamically allocates active channel change servers by analyzing historical data and channel change bias factors, using a neural network to predict and adjust the number of active servers needed for each time slot, thereby optimizing server utilization and avoiding waste or service degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed number of channel change servers are allocated, then system simplicity is maintained, but resource wastage or service degradation occurs due to over- or under-allocation
Solution Approach 1:
The patent implements dynamic server allocation by transitioning from a fixed allocation system to one that automatically adjusts the number of active channel change servers based on real-time demand prediction. The system uses historical data and machine learning models to forecast channel change requests and dynamically scales server resources accordingly, ensuring optimal performance while avoiding resource wastage during low-demand periods.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual channel change request patterns and comparing them with predicted demand. This feedback loop enables the system to refine its predictions and adjust server allocation in real-time, preventing both over-allocation (which causes resource wastage) and under-allocation (which causes service degradation).
2Speed
If more channel change servers are allocated, then channel change speed is improved, but resource wastage increases during low-demand periods
Solution Approach 1:
The system dynamically adjusts the number of active channel change servers based on predicted demand, ensuring that sufficient servers are available during high-demand periods to maintain fast channel changes, while reducing active servers during low-demand periods to prevent resource wastage. This dynamic scaling directly addresses the contradiction between speed and resource efficiency.
Solution Approach 2:
The system performs preliminary actions by predicting future channel change demand using historical data and machine learning models. Based on these predictions, it proactively adjusts server allocation before demand fluctuations occur, ensuring that servers are available when needed for fast channel changes while avoiding unnecessary resource consumption during low-demand periods.
3Loss of energy
If fewer channel change servers are allocated, then resource efficiency is improved, but service degradation occurs due to insufficient server availability
Solution Approach 1:
The system uses feedback from actual channel change request patterns to continuously refine its demand prediction accuracy. This ensures that the number of active servers is always sufficient to maintain service quality during high-demand periods while maximizing resource efficiency during low-demand periods, thus resolving the contradiction between resource efficiency and service reliability.
Solution Approach 2:
By performing preliminary demand prediction using historical data and machine learning, the system proactively ensures that sufficient server capacity is allocated before demand spikes occur. This preliminary action prevents service degradation while optimizing resource efficiency, as servers are only activated when predicted demand requires them.
4Device complexity
If manual operator input is used for server allocation, then system simplicity is maintained, but adaptability to demand changes is reduced
Solution Approach 1:
The system implements self-service by automatically performing demand prediction and server allocation adjustments without requiring manual operator input. The machine learning models autonomously analyze historical data, predict future demand, and dynamically allocate server resources, enabling the system to adapt quickly to changing demand patterns while maintaining operational simplicity for users.
Solution Approach 2:
The system performs preliminary demand analysis and server allocation decisions automatically based on historical patterns and real-time data. This eliminates the need for manual operator intervention while enhancing adaptability to demand changes, as the automated system can respond instantaneously to predicted demand fluctuations without human delay.
Data Source
AI summary
A method includes receiving, at a server, channel change server data. The channel change server data indicates a time and a corresponding estimated number of active channel change servers of a plurality of channel change servers. The method also includes, before the time and in response to a number of active channel change servers being different than the estimated number of active channel change servers, adjusting, via the server, the number of active channel change servers of the plurality of channel change servers to match the estimated number of active channel change servers.