Gateway Selection via Location and Experience Metrics
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Solution Overview
Problem
Network administrators face challenges in identifying and addressing poor performance issues in virtualized applications or desktops due to geographic distances between clients, gateways, and servers, as existing systems lack insights into factors contributing to high latencies and round trip times, leading to sub-optimal user experiences.
Innovation Solution
A performance analytics service that switches clients from one gateway to another based on distance and user experience metrics, using measured performance metrics to correlate location data and identify sub-optimal gateways, thereby providing insights for faster triage and proactive measures to improve network path performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clients connect to geographically distributed gateways to access virtualized applications, then network coverage and accessibility are improved, but round trip time and latency increase due to greater relative distances
Solution Approach 1:
The system dynamically selects gateways based on real-time performance metrics and geographic location data. The gateway selection is not static but adapts continuously by evaluating round trip time, WAN latency, and DC latency measurements to determine the optimal gateway for each client session, thereby reducing latency while maintaining broad network coverage
Solution Approach 2:
The system performs preliminary measurements of round trip time, WAN latency, and DC latency before establishing client connections. By pre-evaluating gateway performance characteristics and geographic distances, the system can proactively assign clients to optimal gateways before performance issues arise, preventing time loss rather than reacting to it
2Device complexity
If network administrators manually monitor performance metrics to identify performance issues, then system complexity is reduced, but response time to address issues increases
Solution Approach 1:
The system automatically monitors performance metrics, correlates location data with performance measurements, and identifies sub-optimal gateways without requiring manual administrator intervention. The automated correlation engine continuously analyzes round trip time, WAN latency, and DC latency data to self-diagnose performance issues and trigger gateway switches, eliminating manual monitoring while maintaining low system complexity through automated rule-based decision-making
Solution Approach 2:
The system implements continuous feedback loops where performance metrics are measured, analyzed, and used to automatically adjust gateway assignments. The correlation between location data and performance measurements provides feedback that drives real-time optimization, enabling rapid response to performance degradation while keeping the system manageable through automated control mechanisms
3Ease of operation
If the system automatically switches clients between gateways based on performance metrics, then user experience is improved, but system complexity increases
Solution Approach 1:
The system introduces a performance analytics service as an intermediary layer between clients and gateways. This mediator automatically correlates location data with performance metrics and manages gateway switching decisions, shielding users from complexity while improving their experience. The intermediary handles the complex correlations and decision-making, presenting only simple, optimized connection paths to end users
Solution Approach 2:
The system changes operational parameters such as round trip time, WAN latency, and DC latency thresholds to trigger automatic gateway switches. By monitoring and responding to parameter changes in performance metrics, the system can automatically optimize user experience without requiring complex manual configurations, using parameter-based rules to manage the switching logic
Data Source
AI summary
Described embodiments provide systems and methods for selecting a device via which to access a server. A service having one or more processors coupled with memory may detect a measure of experience for a client device in accessing a server via a first device being below a threshold. The service may identify, responsive to the detection, a plurality of devices available for the client device to access the server. The service may determine a distance between each of the plurality of devices and at least one of the client device or the server. The service may select a second device from the plurality of devices via which the client device is to access the server based at least on the distance between the second device and at least one of the client device or the server.