Bulk Data Transfer Between Vehicles Using Bandwidth-Aware Connections
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Solution Overview
Problem
Existing systems for transferring bulk data between vehicles of a micro cloud face challenges in efficiently determining the required number of connections and optimizing data transfer within a specific time duration, particularly in congested network conditions.
Innovation Solution
A system that includes an available bandwidth estimation module to estimate the available bandwidth using a model and current network metrics, along with vehicle trajectory information, to determine the necessary number of connections and efficiently transfer bulk data by dividing it into chunks and establishing concurrent connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the number of connections is increased to transfer bulk data within a specific time duration, then the data transfer speed is improved, but the network congestion is worsened
Solution Approach 1:
The system performs preliminary actions by estimating the available bandwidth before actual data transfer begins. The available bandwidth estimation module calculates the optimal number of connections and timing parameters in advance, allowing the system to prepare connection configurations and scheduling decisions before the transfer starts, thus avoiding network congestion while ensuring timely data transmission.
Solution Approach 2:
The system incorporates feedback mechanisms where the available bandwidth estimation module continuously monitors network conditions and adjusts the number of connections and transfer timing accordingly. This feedback loop allows the system to respond to changing network congestion levels and optimize data transfer performance dynamically.
2Productivity
If multiple connections are established to transfer bulk data efficiently, then the productivity is improved, but the device complexity is worsened
Solution Approach 1:
The system segments the bulk data transfer process into multiple independent connections, where each connection handles a portion of the data transfer task. This segmentation allows parallel processing and improves overall productivity while the available bandwidth estimation module manages the complexity by calculating optimal segmentation parameters.
Solution Approach 2:
The available bandwidth estimation module performs self-service by automatically calculating the optimal number of connections and timing parameters without requiring manual intervention. The system self-adjusts connection configurations based on estimated bandwidth conditions, reducing the operational complexity of managing multiple connections.
3Reliability
If the data transfer timing is optimized to avoid congestion, then the reliability is improved, but the transfer time is worsened
Solution Approach 1:
The system performs preliminary bandwidth estimation and timing calculations before data transfer begins. By pre-calculating the optimal transfer schedule and connection configurations based on estimated available bandwidth, the system can proactively avoid congestion periods while minimizing overall transfer time, rather than reacting to congestion after it occurs.
Data Source
AI summary
A system for transferring bulk data between vehicles of a micro cloud can include a processor and a memory. The memory can store an available bandwidth estimation module and a bulk data transfer module. The available bandwidth estimation module can include instructions that cause the processor to produce, using an available bandwidth estimation model, a current network metric, and current vehicle trajectory information, an estimate of an available bandwidth for a transfer of bulk data. The bulk data transfer module can include instructions that cause the processor to: (1) determine, based on the estimate and a size of the bulk data, a number of connections, within a wireless channel of a micro cloud, needed to transfer the bulk data within a specific time duration and (2) cause the transfer of the bulk data from a transmitting vehicle, of the micro cloud, to a receiving vehicle of the micro cloud.