Bandwidth Allocation Prediction for Fixed Networks
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Solution Overview
Problem
Current methods lack an objective and automated way to identify end points in fixed communication networks that require higher bandwidth, relying on educated guesses and manual monitoring.
Innovation Solution
A method involving supervised training of prediction models using datasets that include bandwidth allocation, traffic consumption history, and position within the network, to identify candidate end points likely to need bandwidth increments for a long time period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and educated guesses are used to identify end points needing bandwidth increment, then implementation simplicity is maintained, but identification accuracy and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing telemetry data, traffic consumption history, and bandwidth allocation information before migration decisions are needed. This advance data preparation enables accurate identification of end points requiring bandwidth increments without complex real-time analysis during migration planning.
Solution Approach 2:
A recommendation engine acts as an intermediary between raw network data and migration decisions. This engine processes telemetry data, traffic patterns, and bandwidth information to generate prioritized lists of end points, thereby improving identification accuracy while keeping the overall system architecture manageable through modular design.
2Measurement precision
If higher bandwidth testing is performed on multiple end points simultaneously, then migration prioritization accuracy improves, but network overbooking and test success probability deteriorate
Solution Approach 1:
The system performs preliminary bandwidth capability assessments and telemetry data collection before conducting full bandwidth tests. By pre-identifying promising end points through analysis of traffic consumption history and current bandwidth utilization, the system reduces the number of simultaneous tests needed, thereby maintaining test success probability while improving migration prioritization accuracy.
Solution Approach 2:
The system applies partial action by offering short time trial periods with bandwidth increments to a selected subset of end points rather than testing all end points simultaneously. This selective approach, guided by the recommendation engine's prioritization, maintains acceptable test success probabilities while still achieving accurate migration prioritization through targeted testing.
3Measurement precision
If short time trial periods with bandwidth increments are offered to multiple end points, then identification of high-bandwidth needs improves, but the number of end points that can be tested simultaneously deteriorates
Solution Approach 1:
The system performs preliminary filtering and prioritization using telemetry data and traffic consumption history before initiating bandwidth trial tests. This pre-screening process identifies the most promising end points, allowing the system to maintain high bandwidth need identification accuracy while testing fewer end points simultaneously, thus preserving testing throughput.
Solution Approach 2:
The system applies partial action by selecting a focused subset of end points for bandwidth trial testing based on recommendation engine prioritization. Rather than testing all end points, the system concentrates resources on the most likely candidates, thereby maintaining accurate bandwidth need identification while managing the number of simultaneous tests to preserve overall productivity.
4Reliability
If automated prediction models are implemented, then objectivity and reliability of end point selection improve, but system complexity and implementation difficulty deteriorate
Solution Approach 1:
The system performs preliminary data collection and preparation, organizing telemetry history, traffic consumption data, and bandwidth allocation information into structured formats suitable for prediction model input. This advance preparation enables reliable automated end point selection without requiring complex real-time processing, thereby managing system implementation difficulty.
Solution Approach 2:
A recommendation engine serves as an intermediary layer between complex prediction models and simple decision-making processes. This engine encapsulates the complexity of multiple prediction models and data processing, presenting simplified, reliable recommendations for end point selection, thereby improving reliability while managing perceived system complexity.
Data Source
AI summary
A computer-implemented method for selecting candidates end points includes providing a training success list identifying successful training candidate end points to be allocated a bandwidth increment for a long time period, wherein the successful training candidate end points are a subset of training candidate end points, performing supervised training of a success prediction model for predicting the training success list from the training dataset, determining a predicted list by implementing the success prediction model on a reservoir dataset identifying potential candidate end points belonging to the fixed communications network wherein the predicted list identifies candidates end points as a subset of the potential candidate end points, selecting a candidate list identifying candidate end points to be allocated a bandwidth increment, wherein the candidate end points belong to the fixed communications network, wherein the candidate list includes part or all of the predicted list.