Wireless-Centric Enterprise Network SLA Compliance
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Solution Overview
Problem
Existing technologies face challenges in providing reliable and robust wireless network services that comply with enterprise Internet Service Level Agreements (SLAs), especially in geographically dispersed models, due to issues like network congestion and cross-carrier handling of enterprise traffic.
Innovation Solution
The implementation of a wireless-centric enterprise network model that leverages 5G communication networks to predict and prevent network congestions by dynamically reconfiguring the network and redirecting traffic across different types of networks, including 3GPP wireless networks, trusted/non-trusted non-3GPP wireless networks, and wired networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wireless-centric enterprise network model is implemented to provide flexible configuration and distributed deployment, then adaptability and ease of operation are improved, but network reliability deteriorates due to network congestion and cross-carrier handling issues
Solution Approach 1:
The network management system performs predictive analytics to forecast network congestion before it occurs. By detecting early signs of congestion through machine learning models that analyze historical and real-time network data, the system takes preliminary actions to redirect traffic across different network carriers before the congestion fully develops, thereby maintaining service reliability while preserving wireless network flexibility
Solution Approach 2:
The patent introduces a network management system with predictive analytics capabilities as an intermediary between the wireless network and enterprise traffic. This intermediary monitors network conditions across multiple carriers, predicts congestion events, and automatically redirects traffic through alternative carriers or paths, thus ensuring reliable service delivery without compromising the wireless-centric architecture
2Reliability
If traffic is dynamically redirected across multiple network carriers to prevent congestion, then network reliability is improved, but device complexity increases due to cross-carrier handling requirements
Solution Approach 1:
The network management system employs machine learning models and automated decision-making algorithms that enable the system to self-manage traffic redirection without human intervention. The predictive analytics automatically identify congestion patterns, select appropriate alternative carriers, and execute traffic rerouting decisions, thereby maintaining SLA compliance while minimizing the operational complexity for network administrators
Solution Approach 2:
The system dynamically changes network parameters such as traffic routing paths, carrier selection, and resource allocation based on real-time predictions and SLA requirements. By automatically adjusting these parameters through predictive analytics, the system ensures reliable service delivery while abstracting the complexity of cross-carrier management from human operators
3Reliability
If network resources are optimized in real-time to meet SLA requirements, then service quality is improved, but loss of time increases due to continuous monitoring and reconfiguration needs
Solution Approach 1:
The predictive analytics system continuously analyzes network patterns and forecasts future congestion events before they impact service quality. By predicting potential issues in advance, the system proactively reconfigures network resources and redirects traffic before SLA violations occur, thereby maintaining high service quality while reducing the need for reactive interventions that would consume additional time
Solution Approach 2:
The system implements a closed-loop feedback mechanism where predictive analytics continuously monitor network performance, compare actual results against predicted outcomes, and automatically adjust resource allocation and traffic routing decisions. This real-time feedback loop enables the system to maintain optimal service quality while minimizing the time required for manual monitoring and reconfiguration through automated self-adjustment
Data Source
AI summary
Techniques related to a framework for providing a wireless-centric enterprise network model are disclosed. In one example aspect, a framework for providing a wireless-centric network service to an enterprise in compliance with a Service Level Agreement (SLA) includes a first component in communication with a home wireless network, at least one visited wireless network, and at least one demarcation point of a wired network associated with the enterprise network to predict a future state of the networks. The framework also includes a second component configured to reconfigure traffic associated with the communication session based on the future state predicted by the first component.