Load-Aware Data Slice Swapping in Distributed Databases

The patent changes the balancing parameter from pure capacity-based to load-aware by introducing load proportion as a key decision factor. When selecting data slices for migration, the system evaluates both the capacity distribution and the load characteristics, dynamically adjusting which slices are migrated based on current server load states. This resolves the contradiction by ensuring that data distribution uniformity is achieved without transferring high-load slices to already burdened servers.

The system implements feedback mechanisms by continuously monitoring server load proportions and using this information to guide subsequent data slice migration decisions. After each migration operation, the system reassesses the load state and adjusts its strategy accordingly, preventing the accumulation of excessive load on any single server. This feedback loop ensures that balancing operations maintain both distribution uniformity and system efficiency.

The patent applies partial action by selectively migrating only certain data slices rather than all high-load slices. The system evaluates each candidate slice's load proportion and chooses to migrate only those that will not cause the destination server to become overloaded. This selective approach achieves load balancing while preventing the creation of new bottlenecks, thereby maintaining system efficiency.

The system performs preliminary assessment of the destination server's capacity before initiating data slice migration. By evaluating the server's current load state and predicting the impact of incoming slices, the system prevents overload conditions before they occur. This anti-action approach ensures that load balancing operations do not inadvertently create new performance bottlenecks.

The patent segments the data slice selection process into distinct evaluation stages: identifying candidate slices on source servers, assessing their load proportions, evaluating destination server capacity, and making final migration decisions. This segmentation breaks down the complex multi-server balancing problem into manageable sequential steps, reducing operational complexity while achieving improved load distribution.

The system performs preliminary identification and evaluation of candidate data slices before executing migrations. By pre-assessing which slices are suitable for migration based on load proportions and destination capacity, the system simplifies the actual execution phase. This preliminary action reduces the complexity of real-time decision-making during the balancing operation.