Data Session Scheduling Mechanism for Wireless Network Congestion
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Solution Overview
Problem
In wireless and wired communication networks, users often experience low data rates and lengthy download times due to network congestion and limited capacity, despite advanced technologies like LTE, leading to inefficient network utilization and potential revenue loss during off-peak hours.
Innovation Solution
A method and apparatus that link user data session requirements to network conditions, allowing for delayed data session scheduling based on network load measurements, using tokens and handshaking signals to manage data pipe availability and utilization, ensuring that data sessions are initiated when network conditions are optimal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data sessions are scheduled immediately upon user request, then user response time is improved, but network congestion increases during peak hours
Solution Approach 1:
The system performs preliminary actions by scheduling data sessions in advance during off-peak hours when network conditions are favorable. The scheduler identifies suitable time windows before peak demand occurs and pre-allocates network resources, allowing data transfers to be completed before users actually need them, thus avoiding both congestion and excessive wait times.
Solution Approach 2:
The scheduling mechanism dynamically adjusts data session timing based on real-time network conditions and user requirements. The system continuously monitors network load, user preferences, and deadline constraints, then adaptively modifies when data sessions are initiated and executed, optimizing the balance between user satisfaction and network efficiency.
2Speed
If network capacity is increased to handle peak demand, then user data rates improve, but network resource utilization during off-peak hours decreases
Solution Approach 1:
The system maintains continuous useful action by scheduling data sessions to execute during off-peak periods when network resources are under-utilized. Instead of leaving capacity idle, the scheduler continuously identifies and fills available network bandwidth with deferred data sessions, ensuring resources remain productive throughout the day rather than peaking only during high-demand periods.
Solution Approach 2:
The system changes operational parameters by shifting data session execution times from peak to off-peak hours. The scheduler modifies the timing parameter of data transfers based on network load conditions, user preferences, and deadline constraints, thereby transforming when network resources are consumed without changing the total capacity required.
3Productivity
If data sessions are delayed to optimize network utilization, then network efficiency improves, but user download time increases
Solution Approach 1:
The system performs preliminary scheduling actions by identifying and scheduling data sessions during off-peak hours before peak demand occurs. This advance planning allows the network to efficiently utilize resources during low-load periods while still meeting user deadlines, as the scheduler proactively assigns time slots that optimize both network efficiency and user satisfaction.
Solution Approach 2:
The scheduling mechanism incorporates feedback loops that continuously monitor network conditions, user preferences, and deadline constraints. Based on this feedback, the system dynamically adjusts when data sessions are delayed versus when they are executed immediately, ensuring that delays are only applied when they genuinely improve network efficiency without violating user requirements.
4Productivity
If network scheduling strategies are implemented to optimize wireless utilization, then network capacity utilization improves, but user throughput only benefits when conditions are favorable
Solution Approach 1:
The scheduling system dynamically adapts to varying network conditions and user requirements in real-time. Rather than applying fixed scheduling rules, the mechanism continuously adjusts data session timing based on current network load, user preferences, and deadline constraints, ensuring optimal performance across diverse and changing conditions while maintaining high network utilization.
Solution Approach 2:
The system changes multiple parameters simultaneously including execution timing, priority levels, and resource allocation based on network conditions and user needs. By dynamically modifying these parameters, the scheduler maintains high network capacity utilization while adapting to ensure users receive consistent throughput benefits regardless of when they initiate requests.
Data Source
AI summary
A method and apparatus to link user requirements of data sessions to the network conditions provides an application making an advance request corresponding to a data session, which a communication device may immediately acknowledge but will attend to the request at its convenience. The data session setup is based on the actual network conditions which are either sensed by the device or provided by an agent in the network. The method may be overlaid on top of existing wireless handsets using existing technologies. Hence, all the flexibility and configurability associated with data sessions offered by the existing solutions may remain intact with added features for the customer and the operator. In various exemplary embodiments, the method may use a network load measurement capability in the device and/or a network agent.