GSLB Preferred Backup List Configuration
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Solution Overview
Problem
Existing Global Server Load Balancing (GSLB) systems lack configurability to prioritize backup locations effectively, often relying on static proximity methods that may not align with user preferences, leading to suboptimal service redirection when primary locations are down.
Innovation Solution
Implementing a GSLB policy that allows users to specify a preferred list of backup locations with granular settings, such as country or state-level granularity, enabling directed traffic routing based on custom preferences rather than solely on static proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If static proximity method is used for GSLB, then server selection is automated based on geographic location, but user preference and service optimization are compromised
Solution Approach 1:
The system transitions from static proximity-based server selection to dynamic preference-based selection. Users can configure priority lists of backup locations, and the system dynamically adjusts server selection based on real-time availability and user-defined preferences rather than fixed geographic proximity rules.
Solution Approach 2:
The system changes the selection parameter from automatic geographic proximity to user-configured priority lists. Users can define custom parameters such as country-level or state-level granularity preferences, which override the default static proximity calculation.
2Device complexity
If backup locations are load balanced among all locations, then system simplicity is maintained, but service optimization based on user preferences is lost
Solution Approach 1:
The system segments backup location selection into prioritized groups based on user preferences. Instead of treating all backup locations equally, the system divides them into priority levels (primary, secondary, tertiary) and selects from these segmented groups in order, improving service availability while maintaining manageable complexity through structured configuration.
3Reliability
If granular location preferences are implemented, then service optimization is improved, but configuration complexity increases
Solution Approach 1:
The system provides universal preference configuration capabilities that work across multiple scenarios. Users can define preferences at different granularities (country-level, state-level) and the system automatically adapts the configuration to match the appropriate GSLB service scope, reducing configuration complexity while maintaining optimization benefits.
Data Source
AI summary
The present application is directed towards systems and methods for a user to configure the backup locations to use by an intermediary device providing Global Server Load Balancing (GSLB) services when a primary location is down. In some embodiments, when GSLB is based on static proximity of the location of the client to the GSLB sites and if the primary location is DOWN, then request may be load balanced among all the other locations. But this may not be desirable in many cases. So we need to provide option to the user to specify the preferred list of backup locations to service a client request. The present solution achieves this configurability by using a GSLB policy based on preferred location. One can configure preferred location(s) via a GSLB policy to redirect the client to preferred location(x). One can configure individual policies for different client locations. Based on implementation requirements, one can configure country level granularity, state level granularity and so on.