Distributed Computing Network Reducing Latency in 5G
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Solution Overview
Problem
The existing data transmission processes in wireless communication technologies, such as those used in autonomous driving, result in significant delays due to the long path data must travel from mobile terminals to cloud servers through multiple devices, posing safety hazards due to the lack of timely feedback.
Innovation Solution
A distributed computing network system is implemented where nodes at different layers process and transmit data tasks, allowing nodes to act as both data processors and relays, reducing the burden on upper layers and improving feedback speed by dynamically distributing data processing tasks across multiple levels, including mobile devices as temporary nodes within a 5G wireless network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted through multiple data transfer devices (base stations, equipment rooms) to reach the cloud server, then the data can be processed and fed back, but the transmission delay increases significantly
Solution Approach 1:
The patent segments the centralized cloud computing architecture into a distributed multi-layer computing network. Data processing tasks are divided and distributed across multiple nodes at different layers (access layer, aggregation layer, core layer), allowing parallel processing and reducing the time for data to traverse the entire network path.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying computing nodes across multiple physical locations and network layers. Instead of a single centralized processing point, computation is distributed across a three-dimensional network topology, reducing transmission distances and enabling faster feedback through multiple simultaneous processing paths.
2Productivity
If computing nodes are distributed across multiple layers in the network, then data processing speed improves, but the system complexity increases
Solution Approach 1:
The patent designs network nodes with multi-functional capabilities that can operate at different layers. Each node is equipped with both computing resources for data processing and networking resources for data transmission, allowing the same hardware infrastructure to serve multiple purposes across different network layers without requiring separate specialized equipment.
Solution Approach 2:
The patent implements dynamic task allocation and node selection mechanisms where computing tasks are routed to the most appropriate nodes based on current network conditions, workload distribution, and task requirements. This dynamic approach allows the system to adapt to changing conditions and optimize performance without requiring complex static configurations.
3Productivity
If data processing tasks are distributed across multiple nodes, then the processing capacity increases, but the coordination overhead between nodes increases
Solution Approach 1:
The patent introduces layer-specific aggregation nodes that act as intermediaries between access layer nodes and core layer nodes. These aggregation nodes consolidate data from multiple access nodes, perform local processing, and forward aggregated results upward, reducing the number of direct coordination relationships needed and simplifying the overall coordination overhead.
Data Source
AI summary
The present disclosure provides a distributed computing network system and a method. The distributed computing network may include: a plurality of nodes, where the plurality of nodes is connected through an n-layer network connection. When the distributed computing network receives a data processing task, a node in an ith layer is configured to: receive a data processing task that is corresponding to the ith layer and that is transmitted from a node in a lower layer; complete a data processing task Ti; transmit, to the node in the lower layer, a data processing result; and transmit, to a node in an upper layer, a remaining data processing task, a process of the data processing task meets a preset condition. The overall feedback time of the data processing task is reduced by distributing calculation tasks among multiple layers of nodes. The technology may be applied in both 4G and 5G network.