Cloud Gateway Load Redistribution for Residential Network Bottlenecks
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Solution Overview
Problem
Residential gateways (RGs) often act as bottlenecks in communication networks due to limited computational capabilities, leading to degraded quality of service (QoS) and user experience (QoE) as demand increases, especially with multiple connected devices and data-intensive applications.
Innovation Solution
Implementing a system that analyzes traffic data in home networks to identify performance characteristics and load, allowing for actions to be taken by a remote cloud-based gateway to redistribute processing loads between local and remote RGs, ensuring that the processing load remains within manageable thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a residential gateway is deployed in a home network to provide communication services, then service provisioning capability is improved, but computational resource limitations cause quality of service to degrade under increased demand
Solution Approach 1:
The gateway functionality is segmented into two parts: a local residential gateway that provides basic service provisioning and a remote cloud-based gateway that handles computationally intensive tasks. This segmentation allows the local RG to maintain service capability while offloading heavy processing to the cloud, preventing QoS degradation under increased demand.
Solution Approach 2:
A remote gateway in the cloud acts as an intermediary to supplement the computational capabilities of the local residential gateway. The remote gateway receives data from the local RG, performs additional processing, and returns results, thereby enhancing overall service capability without requiring hardware upgrades to the local device.
2Quantity of substance
If the processing load on a residential gateway increases to support more connected devices, then network coverage and device connectivity are improved, but the bottleneck effect worsens leading to degraded user experience
Solution Approach 1:
The system transitions from a single-dimension local processing model to a two-dimension distributed processing model by adding a remote cloud gateway. This dimensional change allows processing capacity to scale independently of local hardware constraints, enabling support for more devices while maintaining user experience quality.
3Power
If a remote cloud-based gateway is implemented to supplement local RG capabilities, then processing capacity is improved, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where the local residential gateway automatically discovers and connects to available remote gateways in the cloud. The load distribution and coordination are handled autonomously through standardized protocols, reducing the need for complex manual configuration and management despite the distributed architecture.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining data pertaining to a communication service facilitated at least in part by a first gateway located in a building, the communication service involving at least a first user equipment, analyzing the data to determine that a processing load supported by the first gateway exceeds a first threshold, wherein the first threshold is expressed as a function of a capacity of the first gateway, resulting in a determination, and based on the determination, performing an action that causes the processing load supported by the first gateway to be reduced to a level that is less than the first threshold. Other embodiments are disclosed.