Abstract: A method includes determining that a trigger condition of a triggered optimization module of a plurality of optimization modules is met and optimizing scalable storage based on an optimization routine. The optimization routine includes providing a plurality of candidate operations and for a selected optimization module of the plurality of optimization modules that has a higher priority than the triggered optimization module, removing a candidate operation from the plurality of candidate operations that would diminish a modeled state of the scalable storage for the selected optimization module. The optimization routine also includes determining at least one operation of the plurality of candidate operations that would improve the modeled state of the scalable storage for the triggered optimization module and updating the modeled state of the scalable storage to model executing the at least one operation. The method further includes executing the at least one operation.

Abstract: One method includes receiving partitions, generating assignment plans for assignment of the partitions to servers based on dimensional values of dimensions as determined by different assignment heuristics, selecting one of the assignment plans for execution based on analyzing the dimensional values in accordance with the assignment plans, and executing the selected assignment plan on scalable storage. Another method includes determining, for a triggered optimization module that a server is over utilized on a dimension, selecting candidate operations for partitions assigned to the server, for a higher priority optimization module than the triggered optimization module, removing a candidate operation from the candidate operations that would diminish a modeled state of scalable storage, determining an operation of the candidate operations that would improve the modeled state of the scalable storage with respect to a metric of the dimension on the server, and executing the operation on the scalable storage.

Abstract: A method includes determining that a trigger condition of a triggered optimization module of a plurality of optimization modules is met and optimizing scalable storage based on an optimization routine. The optimization routine includes providing a plurality of candidate operations and for a selected optimization module of the plurality of optimization modules that has a higher priority than the triggered optimization module, removing a candidate operation from the plurality of candidate operations that would diminish a modeled state of the scalable storage for the selected optimization module. The optimization routine also includes determining at least one operation of the plurality of candidate operations that would improve the modeled state of the scalable storage for the triggered optimization module and updating the modeled state of the scalable storage to model executing the at least one operation. The method further includes executing the at least one operation.

Abstract: One method includes receiving partitions, generating assignment plans for assignment of the partitions to servers based on dimensional values of dimensions as determined by different assignment heuristics, selecting one of the assignment plans for execution based on analyzing the dimensional values in accordance with the assignment plans, and executing the selected assignment plan on scalable storage. Another method includes determining, for a triggered optimization module that a server is over utilized on a dimension, selecting candidate operations for partitions assigned to the server, for a higher priority optimization module than the triggered optimization module, removing a candidate operation from the candidate operations that would diminish a modeled state of scalable storage, determining an operation of the candidate operations that would improve the modeled state of the scalable storage with respect to a metric of the dimension on the server, and executing the operation on the scalable storage.

Abstract: A correlation data structure that correlates each address of a set of addresses to a corresponding state of multiple possible states. The correlation is maintained by correlating entire groups of addresses to a corresponding group-level state. If all of the addresses in a corresponding group of addresses have the same address-level state, then the group-level state at the root-tier state reflects that. Thus, if an address is accessed that is within that group, no further inquiry is needed. The system concludes the address-level state of the address from the group-level state of the group in which the address resides. If there is a mix in the address-level states amongst the addresses in the corresponding group, then the group level-state at the root-tier correlation may indicate that as well. Thus, further inquiry may be made to identify the address-level state of the address.

Abstract: A correlation data structure that correlates each address of a set of addresses to a corresponding state of multiple possible states. The correlation is maintained by correlating entire groups of addresses to a corresponding group-level state. If all of the addresses in a corresponding group of addresses have the same address-level state, then the group-level state at the root-tier state reflects that. Thus, if an address is accessed that is within that group, no further inquiry is needed. The system concludes the address-level state of the address from the group-level state of the group in which the address resides. If there is a mix in the address-level states amongst the addresses in the corresponding group, then the group level-state at the root-tier correlation may indicate that as well. Thus, further inquiry may be made to identify the address-level state of the address.

Abstract: The present invention extends to methods, systems, and computer program products for updating Bloom filters. Embodiments of the invention facilitate more efficient use Bloom filters across multiple computers connected across a WAN (potentially having limited bandwidth and latency characteristics), such as, for example, computers located on different continents. The acceptability of false positives is leveraged by allowing the operation of removing items from a set to be batched and delayed. On the other hand, insert operations may be more latency sensitive as a delayed insert results in the semantic equivalent to a false negative. As such, additions to a set are processed in closer to real time to update Bloom filters. In some embodiments, Bloom filters are used to check set membership for electronic mail addresses.