Autonomous Traffic Management in Distributed Cloud Mesh Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network architectures for 4G, 5G, and WiFi are not designed to handle distributed traffic efficiently in less resilient, compute-constrained environments, leading to increased capital and operating expenses, latency, and security concerns due to centralized control traffic management and inefficient resource allocation.
Innovation Solution
A computing system with Machine Learning (ML) based modules for autonomous data and signaling traffic management, which dynamically establishes cloud mesh links between service nodes to optimize network parameters and resource utilization across multiple levels of hierarchy, enabling seamless integration and security across heterogeneous and hybrid cloud infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized data-center is used to manage and terminate flow of data and control traffic, then control and management is simplified, but latency increases and network resilience decreases
Solution Approach 1:
The patent segments the centralized core network into multiple distributed edge data centers that can independently handle data and control traffic. This segmentation allows traffic to be processed locally at the edge, reducing latency while maintaining simplified control through standardized interfaces between edge nodes and the core.
Solution Approach 2:
The patent introduces a hierarchical dimension to the network architecture, organizing edge data centers into tiers (first tier, second tier, third tier) based on their proximity to users and interconnection patterns. This dimensional organization enables both local fast processing and coordinated centralized management.
2Loss of time
If multiple edge data centers are deployed to bring data closer to consumption point, then latency is reduced, but device complexity and cost increase
Solution Approach 1:
The patent creates a universal cloud mesh link interface that can be used across all edge data centers regardless of their specific implementation or vendor. This universal interface simplifies the complexity by providing a standardized method for interconnection and traffic management across the distributed architecture.
Solution Approach 2:
The patent implements dynamic traffic steering that automatically routes traffic through optimal paths based on real-time network conditions, load balancing, and proximity. This dynamic behavior reduces the need for complex static configuration and management of multiple edge data centers.
3Ease of operation
If static resource allocation is used in distributed data centers, then management is simplified, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements feedback mechanisms where edge data centers continuously monitor their resource utilization, traffic patterns, and performance metrics. This feedback information is used to dynamically adjust resource allocation and traffic routing decisions, improving efficiency while maintaining simplified management through automated control loops.
Solution Approach 2:
The patent enables dynamic changes in resource allocation parameters based on traffic demand, time of day, and network conditions. This allows the system to adapt resource distribution without complex manual reconfiguration, balancing simplicity of management with efficiency of utilization.
4Manufacturing precision
If centralized control is maintained in distributed architecture, then policy enforcement is consistent, but network resilience decreases due to single point of failure
Solution Approach 1:
The patent pre-configures multiple tier levels of edge data centers with the capability to independently enforce control policies. This preliminary action ensures that even if centralized control is unavailable, edge nodes can continue to operate with consistent policies cached locally, maintaining both consistency and resilience.
Solution Approach 2:
The patent implements redundant interconnections between edge data centers through cloud mesh links, creating backup paths before failures occur. This beforehand cushioning ensures that if one path or node fails, traffic can be rerouted through alternative paths, maintaining network resilience while preserving centralized policy enforcement.
Data Source
AI summary
A system and method for autonomous data and signalling traffic management in a distributed infrastructure is disclosed. The method includes a distributed multi-cloud computing system with machine learning based intelligence across heterogenous computing platforms hosting mobile network functions, capable of leveraging AI based distribution across all the resources to creates autonomous network operations and intelligently work around any impairments. The method includes determining one or more service nodes by using a trained traffic management based ML model and establishing one or more cloud mesh links between the one or more service nodes at multiple levels of hierarchy based on the system, environment and network parameters and the current network demand. Further, the method includes processing the request by providing access of the one or more services hosted on the one or more external devices to the one or more electronic devices via the one or more cloud mesh links.