Network Bandwidth Allocation via Anticipatory Subscriber Data Analysis
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Solution Overview
Problem
Current network bandwidth management systems lack the ability to automatically and timely gather subscriber activity data, aggregate it into historical databases, and use this information for anticipatory control of network operations, leading to inefficient bandwidth allocation and potential service disruptions due to manual intervention and latency in operator adjustments.
Innovation Solution
A system that automatically collects data from client devices, stores it in a historical database, and uses this data to analyze and adjust network operations, including the speculative instantiation or termination of program streams, to optimize bandwidth allocation based on predicted subscriber behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention and analysis of subscriber activity data is used, then network operator can control bandwidth allocation, but latency and lack of timeliness occur leading to inefficient bandwidth utilization
Solution Approach 1:
The network system performs self-monitoring and self-optimization by automatically gathering subscriber activity data from client devices, analyzing it to identify usage patterns, and adjusting bandwidth allocation without requiring manual operator intervention. The system proactively detects bandwidth optimization opportunities and implements corrections automatically, eliminating the latency inherent in manual processes.
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where subscriber activity data is continuously gathered from client devices, analyzed to identify usage patterns, and used to automatically adjust network operations. This feedback loop enables real-time optimization of bandwidth allocation based on actual subscriber behavior, eliminating delays associated with manual analysis and response.
2Extent of automation
If automatic data collection and analysis is implemented, then bandwidth allocation can be optimized in real-time, but system complexity increases
Solution Approach 1:
The server process performs multiple functions within a single integrated system: it gathers subscriber activity data from client devices, stores data in a historical database, analyzes data to identify usage patterns, and automatically adjusts network operations. This multi-functional approach consolidates what would otherwise require separate systems into one unified platform, managing complexity through functional integration rather than multiplication of separate components.
3Measurement precision
If detailed subscriber usage data is collected, then accurate usage patterns can be identified for optimal bandwidth shaping, but data handling complexity and privacy concerns increase
Solution Approach 1:
The system extracts and analyzes only the specific data elements necessary for identifying usage patterns, such as subscriber activity timing and channel selection behavior. By focusing on extracting only the relevant features from subscriber data rather than processing all possible data points, the system achieves accurate usage pattern identification while minimizing data handling complexity and privacy concerns.
Data Source
AI summary
Methods and apparatus for dynamically and automatically allocating network resources through anticipatory analysis and control. In an exemplary embodiment, the network comprises a broadcast switched digital architecture, and network bandwidth allocation to multiple digital program streams is performed by processing historical user tuning data, which is obtained directly from the subscriber's consumer premises equipment (e.g., DSTB). When an increase or decrease in bandwidth required to support certain programs is anticipated, network resource re-allocation is performed automatically by a software process running on the switching server. In this fashion, speculative but “intelligent” projections of bandwidth and program stream requirements can be made automatically by the server software, without operator intervention. The server also optionally dictates the optimal monitoring and data collection parameters to the DSTB.