Global Failover Routing Using Capacity-Aware Rebalance Tables

The patent changes the routing parameters from simple geographic proximity to a composite metric that includes compute capacity availability. The load balancer evaluates multiple data centers based on both geographic distance and available compute capacity, selecting optimal targets that can handle the failover load without causing overload, thus resolving the contradiction between maintaining service continuity and preserving compute capacity.

The system continuously monitors data center health and compute capacity availability, using this feedback to dynamically adjust failover routing decisions. By real-time monitoring of compute capacity metrics, the load balancer can identify data centers that are geographically close but currently overloaded, and redirect traffic to alternative data centers that have sufficient capacity, preventing thundering herd events while maintaining service continuity.

The patent transforms the load distribution approach by changing from simple rotational/round-robin scheduling to a capacity-aware routing mechanism. The load balancer evaluates compute capacity availability as a critical parameter and selects data centers based on both geographic proximity and capacity metrics, preventing the domino effect where major regional data centers become non-responsive while maintaining operational simplicity through automated capacity-based decision making.

The system takes preliminary action by continuously monitoring compute capacity and proactively identifying data centers that are at risk of becoming overloaded during failover events. By detecting capacity constraints in advance and pre-adjusting routing decisions, the load balancer prevents the thundering herd event from escalating into a domino scenario where major data centers become non-responsive, rather than reacting after the problem occurs.

The patent makes existing BSS metering and billing services multi-functional by enabling them to perform both their original functions (metering and billing) and the additional function of data center health monitoring and capacity assessment. This approach allows the system to achieve accurate data center identification without adding separate complex monitoring infrastructure, as the billing service leverages existing data collection and processing capabilities for dual purposes.

The system uses its own existing BSS metering and billing infrastructure to monitor data center health and capacity, rather than requiring external complex monitoring tools. The billing service self-extends its functionality to include health monitoring by leveraging the same data collection mechanisms already in place for metering, thereby achieving accurate measurement without proportionally increasing system complexity or cost.