Predictive Downloading via Low Priority Queues
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Solution Overview
Problem
Current predictive downloading technologies focus on user application levels and fail to consider network load, leading to inefficiencies and unnecessary costs for users, as they are charged for prefetched data regardless of usage.
Innovation Solution
Implement a system that predicts user data needs and transmits prefetched data during network underloads, using a low priority queue and charging users only for data actually consumed, thereby maximizing network throughput and minimizing unnecessary bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If predictive downloading is implemented at user application level, then response time is minimized, but network bandwidth is wasted and users are charged for unused data
Solution Approach 1:
The system performs preliminary actions by predicting what data users will need before they actually request it, and pre-downloads that data during network underloads. This eliminates response time delays while the data is fetched in advance, and the billing mechanism ensures users only pay for data they actually consume, resolving the contradiction between fast response and avoiding bandwidth waste
Solution Approach 2:
The system changes the parameter of data priority by introducing a low priority queue for predictive data, separating it from high priority user requests. This allows predictive data to be transmitted during network underloads without interfering with user experience, while the billing parameter changes to charge only for actually consumed data, not just transmitted data
2Productivity
If predictive data is transmitted during network underloads, then network throughput is maximized, but data transmission may be delayed
Solution Approach 1:
The system performs preliminary data retrieval during network underloads when capacity is available, so that when users actually request the data, it is already cached and can be delivered immediately. This preliminary action maximizes network throughput during low-traffic periods while ensuring zero delay during user requests
Solution Approach 2:
The system dynamically adjusts data transmission timing based on network load conditions. During underloads, predictive data is transmitted to maximize throughput; during high-load periods, the system waits for underloads to occur. This dynamic adaptation allows the system to maximize overall network efficiency without permanently delaying user data requests
3Quantity of substance
If users are charged for prefetched data, then network revenue is increased, but users pay for unnecessary bandwidth usage
Solution Approach 1:
The system implements feedback by tracking whether predictive data is actually consumed by users. The billing mechanism uses this feedback to charge users only for data they actually request and consume, not for data that was merely transmitted during underloads. This resolves the contradiction by aligning bandwidth allocation with actual usage patterns
Data Source
AI summary
An embodiment user equipment has a list of predictive data that a user may request, and programming to receive prefetched data based on the list of predictive data at a reduced cost, wherein the reduced cost is lower than a network cost of downloading the data, and to store the prefetched data within the UE for future consumption. An embodiment base station has a list of predictive data a UE may request, a high priority queue for data requested by the UE, and a low priority queue with predictive data corresponding to the list of predictive data. The base station further includes programing to send the requested data and to send the predictive data.