Application-Aware VPN Routing for Remote Work QoE
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Solution Overview
Problem
Existing remote work setups often fail to consider the impact of enterprise connectivity on application experience for remote workers, neglecting the influence of local/home networks and lacking insight into user experience, leading to suboptimal application performance.
Innovation Solution
A predictive application-aware routing system that utilizes machine learning to analyze network telemetry and application metrics to proactively select the best path for connecting remote workers to online applications, optimizing quality of experience by predicting and preventing SLA violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a remote worker connects to a default VPN location, then security requirements are met, but application performance and quality of experience deteriorate
Solution Approach 1:
The system dynamically selects VPN connection points based on real-time network conditions and application requirements. Instead of using a fixed default VPN location, the system adapts the connection point selection to optimize both security and application performance for each specific scenario.
Solution Approach 2:
The system changes the parameter of VPN connection point selection based on network conditions, application type, and performance metrics. By varying this parameter dynamically, the system achieves optimal balance between security requirements and application performance.
2Reliability
If traffic is routed through a backhaul connection via enterprise datacenter, then security is maintained, but network latency and application responsiveness worsen
Solution Approach 1:
The system introduces an intermediary intelligence layer that sits between the remote worker and the VPN infrastructure. This intermediary analyzes network conditions and selects optimal paths, allowing traffic to bypass suboptimal backhaul routes while maintaining security through controlled access points.
Solution Approach 2:
The system performs preliminary analysis of network conditions and pre-selects optimal VPN connection points before the user establishes a connection. This preliminary action prevents the user from experiencing latency issues by proactively choosing the best available path.
3Device complexity
If the IT personnel have no insight into the local/home network, then system complexity is reduced, but quality of experience and application performance worsen
Solution Approach 1:
The system implements self-service capabilities where the remote worker's device automatically discovers available network interfaces and connectivity options. The device itself provides information about its local network environment, eliminating the need for IT personnel to have direct insight into each user's home network while still optimizing performance.
Solution Approach 2:
The system establishes feedback loops where network performance metrics are continuously monitored and fed back to the routing decision engine. This feedback mechanism allows the system to adapt to local network conditions automatically, improving quality of experience without increasing system complexity.
4Adaptability or versatility
If multiple connectivity options are available, then adaptability and user flexibility improve, but routing complexity and difficulty of selecting optimal path worsen
Solution Approach 1:
The system replaces manual or rule-based routing mechanisms with an intelligent, automated system that uses machine learning and real-time analysis to select optimal paths. This substitution handles the complexity of multiple connectivity options automatically, maintaining user flexibility while eliminating manual routing complexity.
Data Source
AI summary
In one embodiment, a device discovers one or more network interfaces that an endpoint in a local network could use to access an online application. The device identifies a plurality of connectivity options available to the endpoint to access the online application via an external network. The device makes a prediction that a path that comprises a particular connectivity option from among the plurality of connectivity options and a particular network interface from among the one or more network interfaces will provide an optimal quality of experience metric associated with the online application. The device causes the endpoint to use the path to connect to the online application.